U.S. patent application number 14/225298 was filed with the patent office on 2014-07-24 for data classification using machine learning techniques.
This patent application is currently assigned to Kofax, Inc.. The applicant listed for this patent is Kofax, Inc.. Invention is credited to Roland G. Borrey, Anthony Sarah, Mauritius A.R. Schmidtler.
Application Number | 20140207717 14/225298 |
Document ID | / |
Family ID | 39262084 |
Filed Date | 2014-07-24 |
United States Patent
Application |
20140207717 |
Kind Code |
A1 |
Schmidtler; Mauritius A.R. ;
et al. |
July 24, 2014 |
DATA CLASSIFICATION USING MACHINE LEARNING TECHNIQUES
Abstract
Systems, methods and computer program products for classifying
documents are presented. Systems, methods and computer program
products for analyzing documents, e.g. for verifying an association
of an invoice with an entity are also presented. Systems, methods
and computer program products for managing medical records are
presented. One exemplary system includes a memory; and a processor
in communication with the memory, the processor being configured to
process at least some instructions stored in the memory. The memory
stores computer executable program code comprising instructions
for: training a classifier based on an invoice format associated
with a first entity; accessing a plurality of invoices labeled as
being associated with at least one of the first entity and other
entities; and outputting an identifier of at least one of the
invoices having a high probability of not being associated with the
first entity.
Inventors: |
Schmidtler; Mauritius A.R.;
(Escondido, CA) ; Borrey; Roland G.; (Villa Park,
CA) ; Sarah; Anthony; (San Diego, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Kofax, Inc. |
Irvine |
CA |
US |
|
|
Assignee: |
Kofax, Inc.
Irvine
CA
|
Family ID: |
39262084 |
Appl. No.: |
14/225298 |
Filed: |
March 25, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13090216 |
Apr 19, 2011 |
8719197 |
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14225298 |
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11752673 |
May 23, 2007 |
7958067 |
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13090216 |
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60830311 |
Jul 12, 2006 |
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Current U.S.
Class: |
706/12 |
Current CPC
Class: |
G06N 20/00 20190101;
G06N 20/10 20190101; Y02A 90/10 20180101; G16H 50/20 20180101; G06F
16/93 20190101; G06Q 50/18 20130101; G16H 10/60 20180101; G06Q
10/10 20130101 |
Class at
Publication: |
706/12 |
International
Class: |
G06N 99/00 20060101
G06N099/00 |
Claims
1. A system for verifying an association of an invoice with an
entity, comprising: a memory; and a processor in communication with
the memory, the processor being configured to process at least some
instructions stored in the memory, wherein the memory stores
computer executable program code comprising instructions for:
training a classifier based on an invoice format associated with a
first entity; accessing a plurality of invoices labeled as being
associated with at least one of the first entity and other
entities; and outputting an identifier of at least one of the
invoices having a high probability of not being associated with the
first entity.
2. The product of claim 1, wherein the invoice format includes a
physical layout of markings on the invoice.
3. The system of claim 1, further comprising: performing a document
classification technique on the invoices using the classifier.
4. The system of claim 3, wherein the document classification
technique includes a transductive process.
5. The system of claim 4, wherein the invoice format includes a
physical layout of markings on the invoice.
6. The system of claim 3, wherein the document classification
technique includes a support vector machine process.
7. The system of claim 6, wherein the invoice format includes a
physical layout of markings on the invoice.
8. The system of claim 3, wherein the document classification
technique includes a maximum entropy discrimination process.
9. The system of claim 8, wherein the invoice format includes a
physical layout of markings on the invoice.
10. A computer program product comprising a compute readable
storage medium having stored thereon computer executable program
code comprising instructions for: training a classifier based on an
invoice format associated with a first entity; accessing a
plurality of invoices labeled as being associated with at least one
of the first entity and other entities; and outputting an
identifier of at least one of the invoices having a high
probability of not being associated with the first entity.
11. The computer program product of claim 10, wherein the invoice
format includes a physical layout of markings on the invoice.
12. The computer program product of claim 10, further comprising
computer executable program code comprising instructions for:
performing a document classification technique on the invoices
using the classifier.
13. The computer program product of claim 12, wherein the document
classification technique includes a transductive process.
14. The computer program product of claim 13, wherein the invoice
format includes a physical layout of markings on the invoice.
15. The computer program product of claim 12, wherein the document
classification technique includes a support vector machine
process.
16. The computer program product of claim 15, wherein the invoice
format includes a physical layout of markings on the invoice.
17. The computer program product of claim 12, wherein the document
classification technique includes a maximum entropy discrimination
process.
18. The computer program product of claim 17, wherein the invoice
format includes a physical layout of markings on the invoice.
19. A system for managing medical records, comprising: a memory;
and a processor in communication with the memory, the processor
being configured to process at least some instructions stored in
the memory, wherein the memory stores computer executable program
code comprising instructions for: training a classifier based on a
medical diagnosis; accessing a plurality of medical records; and
outputting an identifier of at least one of the medical records
having a low probability of being associated with the medical
diagnosis.
20. The system of claim 19, the memory further comprising computer
executable program code comprising instructions for performing a
document classification technique on the medical records using the
classifier.
21. The system of claim 20, wherein the document classification
technique includes a transductive process.
22. The system of claim 21, wherein the invoice format includes a
physical layout of markings on at least one of the medical
records.
23. The system of claim 20, wherein the document classification
technique includes a support vector machine process.
24. The system of claim 23, wherein the invoice format includes a
physical layout of markings on at least one of the medical
records.
25. The system of claim 20, wherein the document classification
technique includes a maximum entropy discrimination process.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 13/090,216, filed Apr. 19, 2011, which is a
continuation of U.S. patent application Ser. No. 11/752,673 filed
May 23, 2007; each of which claims priority to U.S. Provisional
Patent Application Ser. No. 60/830,311, filed Jul. 12, 2006, and
each of which are herein incorporated by reference.
FIELD OF THE INVENTION
[0002] The present invention relates generally to methods and
apparatus for data classification. More particularly, the present
invention relates to novel applications using machine learning
techniques.
BACKGROUND
[0003] How to handle data has gained in importance in the
information age and more recently with the explosion of electronic
data in all walks of life including, among others, scanned
documents, web material, search engine data, text data, images,
audio data files, etc.
[0004] One area just starting to be explored is the non-manual
classification of data. In many classification methods the machine
or computer must learn based upon manually input and created rule
sets and/or manually created training examples. In machine learning
where training examples are used, the number of learning examples
is typically small compared to the number of parameters that have
to be estimated, i.e. the number of solutions that satisfy the
constraints given by the training examples is large. A challenge of
machine learning is to find a solution that generalizes well
despite the lack of constraints. There is thus a need for
overcoming these and/or other issues associated with the prior
art.
[0005] What is further needed are practical applications for
machine learning techniques of all types.
SUMMARY
[0006] A system for verifying an association of an invoice with an
entity according to another embodiment of the present invention
includes a memory; and a processor in communication with the
memory, the processor being configured to process at least some
instructions stored in the memory, wherein the memory stores
computer executable program code comprising instructions for:
training a classifier based on an invoice format associated with a
first entity; accessing a plurality of invoices labeled as being
associated with at least one of the first entity and other
entities; performing a document classification technique on the
invoices using the classifier; and outputting an identifier of at
least one of the invoices having a high probability of not being
associated with the first entity.
[0007] A computer program product for verifying an association of
an invoice with an entity according to another embodiment of the
present invention includes a program storage medium readable by a
computer, where the medium tangibly embodies one or more programs
of instructions executable by the computer to perform a method,
comprising: training a classifier based on an invoice format
associated with a first entity; accessing a plurality of invoices
labeled as being associated with at least one of the first entity
and other entities; performing a document classification technique
on the invoices using the classifier; and outputting an identifier
of at least one of the invoices having a high probability of not
being associated with the first entity.
[0008] A system for managing medical records according to another
embodiment of the present invention includes a memory; and a
processor in communication with the memory, the processor being
configured to process at least some instructions stored in the
memory, wherein the memory stores computer executable program code
comprising instructions for: training a classifier based on a
medical diagnosis; accessing a plurality of medical records;
performing a document classification technique on the medical
records using the classifier, and outputting an identifier of at
least one of the medical records having a low probability of being
associated with the medical diagnosis.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] FIG. 1 is a depiction of a chart plotting the expected label
as a function of the classification score as obtained by employing
MED discriminative learning applied to label induction.
[0010] FIGS. 2A-H are a depiction of series of plots showing
calculated iterations of the decision function obtained by
transductive MED learning.
[0011] FIGS. 3A-H are a depiction of series of plots showing
calculated iterations of the decision function obtained by the
improved transductive MED learning of one embodiment of the present
invention.
[0012] FIG. 4 illustrates a control flow diagram for the
classification of unlabeled data in accordance with one embodiment
of the invention using a scaled cost factor.
[0013] FIG. 5 illustrates a control flow diagram for the
classification of unlabeled data in accordance with one embodiment
of the invention using user defined prior probability
information.
[0014] FIG. 6 illustrates a detailed control flow diagram for the
classification of unlabeled data in accordance with one embodiment
of the invention using Maximum Entropy Discrimination with scaled
cost factors and prior probability information.
[0015] FIG. 7 is a network diagram illustrating a network
architecture in which the various embodiments described herein may
be implemented.
[0016] FIG. 8 is a system diagram of a representative hardware
environment associated with a user device.
[0017] FIG. 9 illustrates a block diagram representation of the
apparatus of one embodiment of the present invention.
[0018] FIG. 10 illustrates, in a flowchart, a classification
process performed by in accordance with one embodiment.
[0019] FIG. 11 illustrates, in a flowchart, a classification
process performed by in accordance with one embodiment.
[0020] FIG. 12 illustrates, in a flowchart, a classification
process performed by in accordance with one embodiment.
[0021] FIG. 13 illustrates, in a flowchart, a classification
process performed by in accordance with one embodiment.
[0022] FIG. 14 illustrates, in a flowchart, a classification
process performed by in accordance with one embodiment.
[0023] FIG. 15 illustrates, in a flowchart, a classification
process performed by in accordance with one embodiment.
[0024] FIG. 16 illustrates, in a flowchart, a classification
process performed by in accordance with one embodiment.
[0025] FIG. 17 illustrates, in a flowchart, a classification
process performed by in accordance with one embodiment.
[0026] FIG. 18 illustrates, in a flowchart, a classification
process performed by in accordance with one embodiment.
[0027] FIG. 19 illustrates, in a flowchart, a classification
process performed by in accordance with one embodiment.
[0028] FIG. 20 illustrates, in a flowchart, a classification
process performed by in accordance with one embodiment.
[0029] FIG. 21 illustrates, in a flowchart, a classification
process performed by in accordance with one embodiment.
[0030] FIG. 22 illustrates a control flow diagram showing the
method of one embodiment of the present invention applied to a
first document separating system.
[0031] FIG. 23 illustrates a control flow diagram showing the
method of one embodiment of the present invention applied to a
second separating system.
[0032] FIG. 24 illustrates, in a flowchart, a classification
process performed by in accordance with one embodiment.
[0033] FIG. 25 illustrates, in a flowchart, a classification
process performed by in accordance with one embodiment.
[0034] FIG. 26 illustrates, in a flowchart, a classification
process performed by in accordance with one embodiment.
[0035] FIG. 27 illustrates, in a flowchart, a classification
process performed by in accordance with one embodiment.
[0036] FIG. 28 illustrates, in a flowchart, a classification
process performed by in accordance with one embodiment.
[0037] FIG. 29 illustrates, in a flowchart, a classification
process performed by in accordance with one embodiment.
DETAILED DESCRIPTION
[0038] The following description is the best mode presently
contemplated for carrying out the present invention. This
description is made for the purpose of illustrating the general
principles of the present invention and is not meant to limit the
inventive concepts claimed herein. Further, particular features
described herein can be used in combination with other described
features in each of the various possible combinations and
permutations.
[0039] Unless otherwise specifically defined herein, all terms are
to be given their broadest possible interpretation including
meanings implied from the specification as well as meanings
understood by those skilled in the art and as defined in
dictionaries, treatises, etc.
[0040] The interest and need for classification of textual data has
been particularly strong, and several methods of classification
have been employed. A discussion of classification methods for
textual data is described in U.S. Pat. No. 6,192,360 to Dumais, the
content and substance of which is incorporated herein by
reference.
[0041] The current state of the art in commercially used automatic
classification systems is either rule based or utilizes inductive
machine learning, i.e. using manually labeled training examples.
Both methods typically entail a large manual setup effort compared
to transductive methods. The solutions provided by rule based
systems or inductive methods are static solutions that cannot adapt
to drifting classification concepts without manual effort.
[0042] Inductive machine learning is used to ascribe properties or
relations to types based on tokens (i.e., on one or a small number
of observations or experiences); or to formulate laws based on
limited observations of recurring patterns. Inductive machine
learning involves reasoning from observed training cases to create
general rules, which are then applied to the test cases.
[0043] Particularly, preferred embodiments use transductive machine
learning techniques. Transductive machine learning is a powerful
method that does not suffer from these disadvantages.
[0044] Transductive machine techniques may be capable of learning
from a very small set of labeled training examples, automatically
adapting to drifting classification concepts, and automatically
correcting the labeled training examples. These advantages make
transductive machine learning an interesting and valuable method
for a large variety of commercial applications.
[0045] Transduction learns patterns in data. It extends the concept
of inductive learning by learning not only from labeled data but
also from unlabeled data. This enables transduction to learn
patterns that are not or only partly captured in the labeled data.
As a result transduction can, in contrast to rule based systems or
systems based on inductive learning, adapt to dynamically changing
environments. This capability enables transduction to be utilized
for document discovery, data cleanup, and addressing drifting
classification concepts, among other things.
[0046] The following is an explanation of one embodiment of
transductive classification utilizing Support Vector Machine (SVM)
classification as well as the Maximum Entropy Discrimination (MED)
framework.
Support Vector Machines
[0047] Support Vector Machines (SVM) is one employed method of text
classification, and such method approaches the problem of the large
number of solutions and the resulting generalization problem by
deploying constraints on the possible solutions utilizing concepts
of regularization theory. For example, a binary SVM classifier
selects from all hyperplanes that separate the training data
correctly as solution the hyperplane that maximizes the margin. The
maximum margin regularization under the constraint that training
data is classified correctly addresses the aforementioned learning
problem of selecting the appropriate trade-off between
generalization and memorization: The constraint on the training
data memorizes the data, whereas the regularization ensures
appropriate generalization. Inductive classification learns from
training examples that have known labels, i.e. every training
example's class membership is known. Where inductive classification
learns from known labels, transductive classification determines
the classification rules from labeled as well as unlabeled data. An
example of transductive SVM classification is shown in table 1.
Principle of Transductive SVM Classification
TABLE-US-00001 [0048] TABLE 1 Require: Data matrix X of labeled
training examples and their labels Y. Require: Data matrix X' of
the unlabeled training examples. Require: A list of all possible
labels assignments of the unlabeled training examples
[Y.sub.1'.sub., . . . ,Y.sub.n']. 1: MaximumMargin = 0 2: = 0
{Included label assignment of unlabeled training examples.} 3: for
all label assignments Y.sub.i'.sub.,1.ltoreq.i.ltoreq.n in the list
of label assignments do 4: CurrentMaximumMargin :=
MaximizeMargin(X,Y,X',Y.sub.i') 5: if CurrentMaximumMargin >
MaximumMargin then 6: MaximumMargin := CurrentMaximumMargin 7: :=
Y.sub.i' 8: end if 9: end for
[0049] Table 1 shows the principle of a transductive classification
with Support Vector Machines: The solution is given by the
hyperplane that yields the maximum margin over all possible label
assignments of the unlabeled data. The possible label assignments
grow exponentially in the number of unlabeled data and for
practically applicable solutions, the algorithm in Table 1 must be
approximated. An example of such an approximation is described in
T. Joachims, Transductive inference for text classification using
support vector machines, Technical report, Universitaet Dortmund,
LAS VIII, 1999 (Joachims).
[0050] The uniform distribution over label assignments in Table 1
implies that an unlabeled data point has a probability of 1/2 to be
a positive example of the class and a probability of 1/2 of being a
negative example, i.e. its two possible label assignments of y=+1
(positive example) and y=-1 (negative example) are equally likely
and the resulting expected label is zero. A label expectation of
zero can be obtained by a fixed class prior probability equal to
1/2 or a class prior probability that is a random variable with an
uniform prior distribution, i.e. an unknown class prior
probability. Accordingly, in applications with known class prior
probabilities that are not equal to 1/2 the algorithm could be
improved by incorporating this additional information. For example,
instead of using a uniform distribution over label assignments in
Table 1, one could elect to prefer some label assignments over
others according to the class prior probability. However, the
trade-off between a smaller margin solution with a likely label
assignment and a higher margin solution but less likely label
assignment is difficult. The probability of label assignments and
the margin are on different scales.
Maximum Entropy Discrimination
[0051] Another method of classification, Maximum Entropy
Discrimination (MED) (see e.g. T. Jebara, Machine Learning
Discriminative and Generative, Kluwer Academic Publishers) (Jebara)
does not encounter the problems associated with SVMs since the
decision function regularization term as well as the label
assignment regularization term are both derived from prior
probability distributions over solutions and, thus are both on the
same probabilistic scale. Accordingly, if the class priors and,
thus, the label priors are known, transductive MED classification
is superior to transductive SVM classification, since it allows for
the incorporation of prior label knowledge in a principled way.
[0052] Inductive MED classification assumes a prior distribution
over the parameters of the decision function, a prior distribution
over the bias term, and a prior distribution over margins. It
selects as a final distribution over these parameters the one that
is closest to the prior distributions and yields an expected
decision function that classifies the data points correctly.
[0053] Formally, for example given a linear classifier, the problem
is formulated as follows: Find the distribution over hyperplane
parameters p(.THETA.), the bias distribution p(b), the data points
classification margins p(.gamma.) whose combined probability
distribution has a minimal Kullback Leibler divergence KL to the
combined respective prior distributions p.sub.0, i.e.
min p ( .THETA. ) , p ( .gamma. ) , p ( b ) = KL ( p ( .THETA. ) p
( .gamma. ) p ( b ) || p 0 ( .THETA. ) p 0 ( .gamma. ) p 0 ( b ) )
, ( 1 ) ##EQU00001##
subject to the constraint
.A-inverted.t:.intg.d.THETA.d.gamma.dbp(.THETA.)p(.gamma.)p(b)(y.sub.i(.-
THETA.X.sub.i-b))-y.sub.t).gtoreq.0, (2)
where the .THETA.X.sub.t is the dot product between the separating
hyperplane's weight vector and the t-th data point's feature
vector. Since the label assignments y.sub.t are known and fixed, no
prior distribution over the binary label assignments is needed.
Accordingly, a straightforward method to generalize inductive MED
classification to transductive MED classification is to treat the
binary label assignments as parameters that are constrained by a
prior distribution over possible label assignments. An example of
transductive MED is shown in Table 2.
Transductive MED Classification
TABLE-US-00002 [0054] TABLE 2 Require: Data Matrix X of labeled and
unlabeled training examples. Require: Label prior probabilities
p.sub.0(y) for labeled and unlabeled training examples. 1: Y :=
ExpectedLabel(p.sub.0(y)) {Expected label determined from the
training examples' label prior probabilities.} 2: while converged
do 3: W := MinimizeKLDivergence(X, Y ) 4: Y' := InduceLabels(W, X,
p.sub.0(y)) 5: Y :=.epsilon. Y + (1- .epsilon.)Y' 6. end while
[0055] For the labeled data, the label prior distribution is a
.delta. function, thus, effectively fixing the label to be either
+1 or -1. For the unlabeled data, a label prior probability
p.sub.0(y) is assumed that assigns to every unlabeled data point a
positive label of y=+1 with a probability of p.sub.0(y) and a
negative label of y=-1 with a probability of 1-p.sub.0(y). Assuming
a noninformative label prior (p.sub.0(y)=1/2), yields a
transductive MED classification analogous to the transductive SVM
classification discussed above.
[0056] As in the case of the transductive SVM classification, a
practically applicable implementation of such an MED algorithm must
approximate the search through all possible label assignments. The
method described in T. Jaakkola, M. Meila, and T. Jebara, Maximum
entropy discrimination, Technical Report AITR-1668, Massachusetts
Institute of Technology, Artificial Intelligence Laboratory, 1999
(Jaakkola) elects as an approximation to decompose the procedure
into two steps, similar to an Expectation Maximization (EM)
formulation. In this formulation, there are two problems to solve.
The first, analogous to the M step in EM algorithms, is similar to
the maximization of the margin while classifying all data points
correctly according to the current best guess of label assignments.
The second step, analogous to the E step, uses the classification
results determined in the M step and estimates new values for each
example's class membership. This second step we call label
induction. A general description is shown in Table 2.
[0057] The specific implementation of the method of Jaakkola,
referenced herein, assumes a Gaussian with zero mean and unit
variance for the hyperplane parameters, a Gaussian with zero mean
and variance .sigma..sub.b.sup.2 for the bias parameter, a margin
prior of the form exp [-c(1-.gamma.)] with .gamma. a data point's
margin and c the cost factor, and a binary label prior probability
of p.sub.0(y) for unlabeled data as discussed above. For the
following discussion of the transductive classification algorithm
Jaakkola, referenced herein, a label prior probability of 1/2 is
assumed for reasons of simplicity and without loss of
generality.
[0058] The label induction step determines the label probability
distribution given a fixed probability distribution for the
hyperplane parameters. Using the margin and label priors introduced
above yields the following objective function for the label
induction step (see Table 2)
( .lamda. ) = ( .lamda. t + log ( 1 - .lamda. t / x ) ) - log cosh
( .lamda. t s t ) , ( 3 ) ##EQU00002##
where .lamda..sub.t is the t-th training example Lagrange
Multiplier, and s.sub.t its classification score determined in the
previous M-step, and c the cost factor. The first two terms in the
sum over the training examples is derived from the margin prior
distribution, whereas the third term is given by the label prior
distribution. By maximizing I the Lagrange Multipliers are
determined and, thus, the label probability distributions for the
unlabeled data. As can be seen from Eq. 3 the data points
contribute independently to the objective function and, thus, each
Lagrange Multiplier can be determined irrespective of every other
Lagrange Multiplier. For example, in order to maximize the
contribution of an unlabeled data point with a high absolute value
of its classification score |s.sub.t| a small Lagrange Multiplier
.lamda..sub.t is required, whereas an unlabeled data point with a
small value of |s.sub.t| maximizes its contribution to I with a
large Lagrange Multiplier. On the other hand, the expected label y
of an unlabeled data point as a function or its classification
score s and its Lagrange Multiplier .lamda. is
y=tan h(.lamda.s). (4)
[0059] FIG. 1 shows the expected label y as a function of the
classification score s using the cost factor of c=5 and c=1.5. The
Lagrange Multipliers used in the generation of FIG. 1 have been
determined by solving Eq. 3 using a cost factor of c=5 and c=1.5.
As can be seen from FIG. 1, unlabeled data points outside the
margin, i.e. |s|>1, have expected labels y close to zero, data
points close to the margin, i.e. |s|.apprxeq.1, yield the highest
absolute expected label values, and data points close to the
hyperplane, i.e. |s|<.epsilon., yield |y|<.epsilon.. The
reason for this unintuitive label assignment of y.fwdarw.0 for
|s|.fwdarw..infin. lies within the elected discriminative approach
that attempts to stay as close as possible to the prior
distribution as long as the classification constraints are
fulfilled. It is not an artifact of the approximation elected by
the known method of Table 2, i.e. an algorithm that exhaustively
searches through all possible label assignments and, thus, has the
guarantee to find the global optimum also assigns unlabeled data
outside the margin expected labels either close to or equal to
zero. Again, as mentioned above, that is expected from a
discriminative point of view. Data points outside the margin are
not important for separating the examples and, thus, all individual
probability distributions of these data points revert back to their
prior probability distribution.
[0060] The M step of the transductive classification algorithm of
Jaakkola, referenced herein, determines the probability
distributions for the hyperplane parameters, the bias term, and
margins of the data points that are closest to the respective prior
distribution under the constraints
.A-inverted.t:s.sub.ty.sub.t-.gamma..sub.t.gtoreq.0, (5)
where s.sub.t is the t-th data point classification score, y.sub.t
its expected label and .gamma..sub.t its expected margin. For
labeled data, the expected label is fixed and either y=+1 or y=-1.
The expected label for unlabeled data lies in the interval (-1, +1)
and is estimated in the label induction step. According to Eq. 5
unlabeled data have to fulfill tighter classification constraints
than labeled data since the classification score is scaled by the
expected label. Furthermore, given the dependence of the expected
label as a function of the classification score, referring to FIG.
1, unlabeled data close to the separating hyperplane have the most
stringent classification constraints since their score as well as
the absolute value of their expected label |y.sub.t| is small. The
M step's full objective function given the prior distributions
mentioned above is
( .lamda. ) = - 1 2 t , t ' y t y t ' .lamda. t .lamda. t ' K ( X t
, X t ' ) + t ( .lamda. t + log ( 1 - .lamda. t / c ) ) - 1 2 (
.sigma. b t y t .lamda. t ) 2 . ( 6 ) ##EQU00003##
[0061] The first term is derived from the Gaussian hyperplane
parameters prior distribution, the second term is the margin prior
regularization term and the last term is the bias prior
regularization term derived from a Gaussian prior with zero mean
and variance .sigma..sub.b.sup.2. The prior distribution over the
bias term can be interpreted as a prior distribution over class
prior probabilities. Accordingly, the regularization term that
corresponds to the bias prior distribution constrains the weight of
the positive to negative examples. According to Eq. 6, the
contribution of the bias term is minimized in case the collective
pull of the positive examples on the hyperplane equals the
collective pull of the negative examples. The collective constraint
on the Lagrange Multipliers owing to the bias prior is weighted by
the expected label of the data points and is, therefore, less
restrictive for unlabeled data than for labeled data. Thus,
unlabeled data have the ability of influencing the final solution
stronger than the labeled data.
[0062] In summary, at the M step of the transductive classification
algorithm of Jaakkola, referenced herein, unlabeled data have to
fulfill stricter classification constraints than the labeled data
and their cumulative weight to the solution is less constrained
than for labeled data. In addition, unlabeled data with an expected
label close to zero that lie within the margin of the current M
step influence the solution the most. The resulting net effect of
formulating the E and M step this way is illustrated by applying
this algorithm to the dataset shown in FIGS. 2A-H. The dataset
includes two labeled examples, a negative example (x) at x-position
-1 and a positive example (+) at +1, and six unlabeled examples (o)
between -1 and +1 along the x-axis. The cross (x) denotes a labeled
negative example, the plus sign (+) a labeled positive example, and
the circles (o) unlabeled data. The different plots show separating
hyperplanes determined at various iterations of the M step. The
final solution elected by the transductive MED classifier of
Jaakkaola, referenced herein, misclassifies the positive labeled
training example. FIGS. 2A-H shows several iterations of the M
step. At the first iteration of the M step, no unlabeled data are
considered and the separating hyperplane is located a x=0. The one
unlabeled data point with a negative x-value is closer than any
other unlabeled data to this separating hyperplane. At the
following label induction step, it will get assigned the smallest y
and, accordingly, at the next M step it has the most power to push
the hyperplane towards the positive labeled example. The specific
shape of the expected label y as a function of the classification
score determined by the chosen cost factor (see FIG. 1) combined
with the particular spacing of the unlabeled data points creates a
bridge effect, where at each consecutive M step the separating
hyperplane is moving closer and closer towards the positive labeled
example. Intuitively, the M step suffers from a kind of short
sightedness, where the unlabeled data point closest to the current
separating hyperplane determines the final position of the plane
the most and the data points further away are not very important.
Finally, owing to the bias prior term that restricts the collective
pull of unlabeled data less than the collective pull of the labeled
data, the separating hyperplane moves beyond the positive labeled
example yielding a final solution, 15-th iteration in FIGS. 2A-H,
that misclassifies the positive labeled example. A bias variance of
.sigma..sub.b.sup.2=1 and a cost factor of c=10 has been used in
FIGS. 2A-H. With .sigma..sub.b.sup.2=1 any cost factor in the range
9.8<c<13 results in a final hyperplane that misclassifies the
one positive labeled example. Cost factors outside the interval
9.8<c<13 yield separating hyperplanes anywhere between the
two labeled examples.
[0063] This instability of this algorithm is not restricted to the
example shown in FIGS. 2A-H, but also has been experienced while
applying the Jaakkola method, referenced herein, to real world
datasets, involving the Reuters data set known to those skilled in
the art. The inherent instability of the method described in Table
2 is a major shortcoming of this implementation and restricts its
general usability, though the Jaakkola method may be implemented in
some embodiments of the present invention.
[0064] One preferred approach of the present invention employs
transductive classification using the framework of Maximum Entropy
Discrimination (MED). It should be understood that various
embodiments of the present invention, while applicable for
classification may also be applicable to other MED learning
problems using transduction, including, but not limited to
transductive MED regression and graphical models.
[0065] Maximum Entropy Discrimination constrains and reduces the
possible solutions, by assuming a prior probability distribution
over the parameters. The final solution is the expectation of all
possible solutions according to the probability distribution that
is closest to the assumed prior probability distribution under the
constraint that the expected solution describes the training data
correctly. The prior probability distribution over solutions maps
to a regularization term, i.e. by choosing a specific prior
distribution one has selected a specific regularization.
[0066] Discriminative estimation as applied by Support Vector
Machines is effective in learning from few examples. This method
and apparatus of one embodiment of the present invention has this
in common with Support Vector Machines and does not attempt to
estimate more parameters than necessary for solving the given
problem and, consequently, yields a sparse solution. This is in
contrast to generative model estimation that attempts to explain
the underlying process and, in general needs higher statistics than
discriminative estimation. On the other hand, generative models are
more versatile and can be applied to a larger variety of problems.
In addition, generative model estimation enables straightforward
inclusion of prior knowledge. The method and apparatus of one
embodiment of the present invention using Maximum Entropy
Discrimination bridges the gap between pure discriminative, e.g.
Support Vector Machine learning, and generative model
estimation.
[0067] The method of one embodiment of the present invention as
shown in Table 3 is an improved transductive MED classification
algorithm that does not have the instability problem of the method
discussed in Jaakkola, referenced herein. Differences include, but
are not limited to, that in one embodiment of the present invention
every data point has its own cost factor proportional to its
absolute label expectation value |y|. In addition, each data points
label prior probability is updated after each M step according to
the estimated class membership probability as function of the data
point's distance to the decision function. The method of one
embodiment of the present invention is described in Table 3 as
follows:
Improved Transductive MED Classification
TABLE-US-00003 [0068] TABLE 3 Require: Data matrix X of labeled and
unlabeled training examples Require: Label prior probabilities
p.sub.0(y) for labeled and unlabeled training examples. Require:
Global cost factor c: 1: Y : ExpectedLabel(p.sub.0(y)) {Expected
label determined from the training examples' label prior
probabilities.} 2: while converged do 3: C:= | Y |c {Scale each
training example's cost factor by the absolute value of its
expected label.} 4: W := MinimizeKLDivergence(X, Y ,C) 5:
p.sub.0(y) := EstimateClassProbability(W, Y ) 6: Y' :=
InduceLabels(W, X, p.sub.0(y), C) 7: Y :=.epsilon. Y + (1-
.epsilon.)Y' 8: end while
[0069] Scaling the data points cost factors by |y| mitigates the
problem that the unlabeled data can have a stronger cumulative pull
on the hyperplane than the labeled data, since the cost factors of
unlabeled data are now smaller than labeled data cost factors, i.e.
each unlabeled data point's individual contribution to the final
solution is always smaller than labeled data points individual
contribution. However, in case the amount of unlabeled data is much
larger then the number of labeled data, the unlabeled data still
can influence the final solution more than the labeled data. In
addition, the conjunction of cost factor scaling with updating the
label prior probability using the estimated class probability
solves the problem of the bridge effect outlined above. At the
first M steps, unlabeled data have small cost factors yielding an
expected label as function of the classification score that is very
flat (see FIG. 1) and, accordingly, to some extent all unlabeled
data are allowed to pull on the hyperplane, albeit only with small
weight. In addition, owing to the updating of the label prior
probability, unlabeled data far away from the separating hyperplane
do not get assigned an expected label close to zero, but after
several iterations a label close to either y=+1 of y=-1 and, thus,
are slowly treated like labeled data.
[0070] In a specific implementation of the method of one embodiment
of the present invention, by assuming a Gaussian prior with zero
mean and unit variance for the decision function parameters
.THETA.
p 0 ( .THETA. ) = 1 ( 2 .pi. ) n - 1 2 .THETA. t .THETA. , . ( 7 )
##EQU00004##
[0071] The prior distribution over decision function parameters
incorporates important prior knowledge of the specific
classification problem at hand. Other prior distributions of
decision function parameters important for classification problem
are for example a multinomial distribution, a Poisson distribution,
a Cauchy distribution (Breit-Wigner), a Maxwell-Boltzman
distribution or a Bose-Einstein distribution.
[0072] The prior distribution over the threshold b of the decision
function is given by a Gaussian distribution with mean .mu..sub.b
and variance .sigma..sub.b.sup.2
p 0 ( b ) = 1 2 .pi. .sigma. b - 1 ( b - .mu. b ) 2 2 .sigma. b 2 .
( 8 ) ##EQU00005##
[0073] As prior distribution of a data point's classification
margin .gamma..sub.t
p 0 ( .gamma. t ) = c - c ( 1 + 1 c - .gamma. t ) , ( 9 )
##EQU00006##
[0074] Was elected, where c is the cost factor. This prior
distribution differs from the one used in Jaakkola, referenced
herein, which has the form exp[-c(1-.gamma.)]. Preferably, the form
given in Eq. 9 over the form used in Jaakkola, referenced herein,
since it yields a positive expected margin even for cost factor
smaller than one, whereas exp [-c(1-.gamma.)] yields a negative
expected margin for c<1.
[0075] Given these prior distributions, determining the
corresponding partition functions Z is straightforward (see for
example T. M. Cover and J. A. Thomas, Elements of Information
Theory, John Wiley & Sons, Inc.) (Cover), and the objective
functions I=-log Z are
.THETA. ( .lamda. ) = - 1 2 t , t ' y t y t ' .lamda. t .lamda. t '
K ( X t , X t ' ) b ( .lamda. ) = - .sigma. b 2 2 ( t .lamda. t y t
) 2 - .mu. b t .lamda. t y t .gamma. ( .lamda. ) = t ( 1 + 1 c )
.lamda. t + log ( 1 - .lamda. t c ) . ( 10 ) ##EQU00007##
[0076] According to Jaakkola, referenced herein the objective
function of the M step is
I.sub.M(.lamda.)=I.sub..THETA.(.lamda.)+I.sub.b(.lamda.)+I.sub..gamma.(.-
lamda.) (11)
and the E step's objective function is
E ( .lamda. ) = .gamma. ( .lamda. ) - t log y t = .+-. 1 p 0 , t (
y t ) y t .lamda. t s t , ( 12 ) ##EQU00008##
where s.sub.t is the t-th data point's classification score
determined in the previous M step and p.sub.0,t(y.sub.t) the data
point's binary label prior probability. The label prior is
initialized to p.sub.0,t(y.sub.t)=1 for labeled data and to either
the non-informative prior of p.sub.0,t(y.sub.t)=1/2 or the class
prior probability for unlabeled data.
[0077] The section herein entitled M STEP describes the algorithm
to solve the M step objective function. Also, the section herein
entitled E STEP describes the E step algorithm.
[0078] The step EstimateClassProbability in line 5 of Table 3 uses
the training data to determine the calibration parameters to turn
classification scores into class membership probabilities, i.e. the
probability of the class given the score p(c|s). Relevant methods
for estimating the score calibration to probabilities are described
in J. Platt, Probabilistic outputs for support vector machines and
comparison to regularized likelihood methods, pages 61-74, 2000
(Platt) and B. Zadrozny and C. Elkan. Transforming classifier
scores into accurate multi-class probability estimates, 2002
(Zadrozny).
[0079] Referring particularly to FIGS. 3A-H, the cross (x) denotes
a labeled negative example, the plus sign (+) a labeled positive
example, and the circles (o) unlabeled data. The different plots
show separating hyperplanes determined at various iterations of the
M step. The 20-th iteration shows the final solution elected by the
improved transductive MED classifier. FIGS. 3A-H shows the improved
transductive MED classification algorithm applied to the toy
dataset introduced above. The parameters used are c=10,
.sigma..sub.b.sup.2=1, and .mu..sub.b=0. Varying c yields
separating hyperplanes that are located between x.apprxeq.-0.5 and
x=0, whereby with c<3.5 the hyperplane is located right to the
one unlabeled data with x<0 and with c.gtoreq.3.5 left to this
unlabeled data point.
[0080] Referring particularly to FIG. 4, a control flow diagram is
illustrated showing the method of classification of unlabeled data
of one embodiment of the present invention. The method 100 begins
at step 102 and at step 104 accesses stored data 106. The data is
stored at a memory location and includes labeled data, unlabeled
data and at least one predetermined cost factor. The data 106
includes data points having assigned labels. The assigned labels
identify whether a labeled data point is intended to be included
within a particular category or excluded from a particular
category.
[0081] Once data is accessed at step 104, the method of one
embodiment of the present invention at step 108 then determines the
label prior probabilities of the data point using the label
information of data point. Then, at step 110 the expected labels of
the data point are determined according to the label prior
probability. With the expected labels calculated in step 110, along
with the labeled data, unlabeled data and cost factors, step 112
includes iterative training of the transductive MED classifier by
the scaling of the cost factor unlabeled data points. In each
iteration of the calculation the unlabeled data points' cost
factors are scaled. As such, the MED classifier learns through
repeated iterations of calculations. The trained classifier then
accessed input data 114 at step 116. The trained classifier can
then complete the step of classifying input data at step 118 and
terminates at step 120.
[0082] It is to be understood that the unlabeled data of 106 and
the input data 114 may be derived from a single source. As such,
the input data/unlabeled data can be used in the iterative process
of 112 which is then used to classify at 118. Furthermore, one
embodiment of the present invention contemplates that the input
data 114 maybe include a feedback mechanism to supply the input
data to the stored data at 106 such that the MED classifier of 112
can dynamically learn from new data that is input.
[0083] Referring particularly to FIG. 5, a control flow diagram is
illustrated showing another method of classification of unlabeled
data of one embodiment of the present invention including user
defined prior probability information. The method 200 begins at
step 202 and at step 204 accesses stored data 206. The data 206
includes labeled data, unlabeled data, a predetermined cost factor,
and prior probability information provided by a user. The labeled
data of 206 includes data points having assigned labels. The
assigned labels identify whether the labeled data point is intended
to be included within a particular category or excluded from a
particular category.
[0084] At step 208, expected labels are calculated from the data of
206. The expected labels then used in step 210 along with labeled
data, unlabeled data and cost factors to conduct iterative training
of a transductive MED classifier. The iterative calculations of 210
scale the cost factors of the unlabeled data at each calculation.
The calculations continue until the classifier is properly
trained.
[0085] The trained classifier then accessed input data at 214 from
input data 212. The trained classifier can then complete the step
of classifying input data at step 216. As with the process and
method described in FIG. 4, the input data and the unlabeled data
may derive from a single source and may be put into the system at
both 206 and 212. As such, the input data 212 can influence the
training at 210 such that the process my dynamically change over
time with continuing input data.
[0086] In both methods as shown in FIGS. 4 and 5 a monitor may
determine whether or not the system has reached convergence.
Convergence may be determined when the change of the hyperplane
between each iteration of the MED calculation falls below a
predetermined threshold value. In an alternative embodiment of the
present invention, the threshold value can be determined when the
change of the determined expected label falls below a predetermined
threshold value. If convergence is reached, then the iterative
training process may cease.
[0087] Referring particularly to FIG. 6, illustrated is a more
detailed control flow diagram of the iterative training process of
at least one embodiment of the method of the present invention. The
process 300 commences at step 302 and at step 304 data is accessed
from data 306 and may include labeled data, unlabeled data, at
least one predetermined cost factor, and prior probability
information. The labeled data points of 306 include a label
identifying whether the data point is a training example for data
points to be included in the designated category or a training
example for data points to be excluded form a designated category.
The prior probability information of 306 includes the probability
information of labeled data sets and unlabeled data sets.
[0088] In step 308, expected labels are determined from the data
from the prior probability information of 306. In step 310, the
cost factor is scaled for each unlabeled data set proportional to
the absolute value of the expected label of a data point. An MED
classifier is then trained in step 312 by determining the decision
function that maximizes the margin between the included training
and excluded training examples utilizing the labeled as well as the
unlabeled data as training examples according to their expected
labels. In step 314 classification scores are determined using the
trained classifier of 312. In 316 classification scores are
calibrated to class membership probability. In step 318, label
prior probability information is updated according to the class
membership probability. An MED calculation is preformed in step 320
to determine label and margin probability distributions, wherein
the previously determined classification scores are used in the MED
calculation. As a result, new expected labels are computed at step
322 and the expected labels are updated in step 324 using the
computations from step 322. At step 326 the method determines
whether convergence has been achieved. If so, the method terminates
at step 328. If convergence is not reached, another iteration of
the method is completed starting with step 310. Iterations are
repeated until convergence is reached thus resulting in an
iterative training of the MED classifier. Convergence may be
reached when change of the decision function between each iteration
of the MED calculation falls below a predetermined value. In an
alternative embodiment of the present invention, convergence may be
reached when the change of the determined expected label value
falls below a predetermined threshold value.
[0089] FIG. 7 illustrates a network architecture 700, in accordance
with one embodiment. As shown, a plurality of remote networks 702
are provided including a first remote network 704 and a second
remote network 706. A gateway 707 may be coupled between the remote
networks 702 and a proximate network 708. In the context of the
present network architecture 700, the networks 704, 706 may each
take any form including, but not limited to a LAN, a WAN such as
the Internet, PSTN, internal telephone network, etc.
[0090] In use, the gateway 707 serves as an entrance point from the
remote networks 702 to the proximate network 708. As such, the
gateway 707 may function as a router, which is capable of directing
a given packet of data that arrives at the gateway 707, and a
switch, which furnishes the actual path in and out of the gateway
707 for a given packet.
[0091] Further included is at least one data server 714 coupled to
the proximate network 708, and which is accessible from the remote
networks 702 via the gateway 707. It should be noted that the data
server(s) 714 may include any type of computing device/groupware.
Coupled to each data server 714 is a plurality of user devices 716.
Such user devices 716 may include a desktop computer, lap-top
computer, hand-held computer, printer or any other type of logic.
It should be noted that a user device 717 may also be directly
coupled to any of the networks, in one embodiment.
[0092] A facsimile machine 720 or series of facsimile machines 720
may be coupled to one or more of the networks 704, 706, 708.
[0093] It should be noted that databases and/or additional
components may be utilized with, or integrated into, any type of
network element coupled to the networks 704, 706, 708. In the
context of the present description, a network element may refer to
any component of a network.
[0094] FIG. 8 shows a representative hardware environment
associated with a user device 716 of FIG. 7, in accordance with one
embodiment. Such Fig. illustrates a typical hardware configuration
of a workstation having a central processing unit 810, such as a
microprocessor, and a number of other units interconnected via a
system bus 812.
[0095] The workstation shown in FIG. 8 includes a Random Access
Memory (RAM) 814, Read Only Memory (ROM) 816, an I/O adapter 818
for connecting peripheral devices such as disk storage units 820 to
the bus 812, a user interface adapter 822 for connecting a keyboard
824, a mouse 826, a speaker 828, a microphone 832, and/or other
user interface devices such as a touch screen and a digital camera
(not shown) to the bus 812, communication adapter 834 for
connecting the workstation to a communication network 835 (e.g., a
data processing network) and a display adapter 836 for connecting
the bus 812 to a display device 838.
[0096] Referring particularly to FIG. 9 there is shown the
apparatus 414 of one embodiment of the present invention. One
embodiment of the present invention comprises in memory device 404
for storing labeled data 416. The labeled data points 416 each
include a label indicating whether the data point is a training
example for data points being included in the designated category
or a training example for data points being excluded from a
designated category. Memory 404 also stores unlabeled data 418,
prior probability data 420 and the cost factor data 422.
[0097] The processor 402 accesses the data from the memory 404 and
using transductive MED calculations trains a binary classifier
enable it to classify unlabeled data. The processor 402 uses
iterative transductive calculation by using the cost factor and
training examples from labeled and unlabeled data and scaling that
cost factor as a function of expected label value thus effecting
the data of the cost factor data 422 which is then re-input into
processor 402. Thus the cost factor 422 changes with each iteration
of the MED classification by the processor 402. Once the processor
402 adequately trains an MED classifier, the processor can then
construct the classifier to classify the unlabeled data into
classified data 424.
[0098] Transductive SVM and MED formulations of the prior art lead
to an exponential growth of possible label assignments and
approximations have to be developed for practical applications. In
an alternative embodiment of the present invention, a different
formulation of the transductive MED classification is introduced
that does not suffer from an exponential growth of possible label
assignments and allows a general closed form solution. For a linear
classifier the problem is formulated as follows: Find the
distribution over hyperplane parameters p(.THETA.), the bias
distribution p(b), the data points classification margins
p(.gamma.) whose combined probability distribution has a minimal
Kullback Leibler divergence KL to the combined respective prior
distributions p.sub.0, i.e.
min p ( .THETA. ) , p ( .gamma. ) , p ( b ) = KL ( p ( .THETA. ) p
( .gamma. ) p ( b ) || p 0 ( .THETA. ) p 0 ( .gamma. ) p 0 ( b ) )
, ( 13 ) ##EQU00009##
subject to the following constraint for the labeled data
.A-inverted.t:.intg.d.THETA.d.gamma.dbp(.THETA.)p(.gamma.)p(b)(y.sub.t(.-
THETA.X.sub.t-b))-.gamma..sub.t).gtoreq.0, (14)
and subject to the following constraint for the unlabeled data
.A-inverted.t':.intg.d.THETA.d.gamma.dbp(.THETA.)p(.gamma.)p(b)((.THETA.-
X.sub.t'-b)).sup.2-.gamma..sub.t').gtoreq.0, (15)
where the .THETA.X.sub.t is the dot product between the separating
hyperplane's weight vector and the t-th data point's feature
vector. No prior distribution over labels is necessary. The labeled
data are constrained to be on the right side of the separating
hyperplane according to their known labels, whereas the only
requirement for the unlabeled data is that their squared distance
to the hyperplane is greater than the margin. In summary this
embodiment of the present invention finds a separating hyperplane
that is a compromise of being closest to the chosen prior
distribution, separating the labeled data correctly, and having no
unlabeled data between the margins. The advantage is that no prior
distribution over labels has to be introduced, thus, avoiding the
problem of exponentially growing label assignments.
[0099] In specific implementation of the alternate embodiment of
the present invention, using the prior distributions given in the
Eqs. 7, 8, and 9 for the hyperplane parameters, the bias, and the
margins yields the following partition function
Z ( .lamda. ) = 1 ( 2 .pi. ) n + 1 .sigma. b .intg. .THETA. b - 1 2
.THETA. T .THETA. - 1 2 ( b - .mu. b .sigma. b ) 2 + t .lamda. t y
t ( .THETA. T X t - b ) + t ' .lamda. t ' ( .THETA. T X t - b ) 2 (
t .intg. p 0 ( .gamma. t ) t .lamda. t .gamma. t .gamma. t ) ( t '
.intg. p 0 ( .gamma. t ' ) t ' .lamda. t ' .gamma. t ' .gamma. t '
) , ( 16 ) ##EQU00010##
where subscript t is the index of the labeled data and t' the index
of the unlabeled data. Introducing the notation
Z = ( .THETA. b - .mu. b ) , U = ( X - 1 ) , G 1 = ( 1 0 0 0 1 0 1
.sigma. b 2 ) , G 2 = t ' U t ' U y ' T , G 3 = G 1 - 2 G 2 , and W
= t .lamda. t .gamma. t U t - 2 t ' .lamda. t ' .gamma. t ' U t ' ,
( 17 ) ##EQU00011##
Eq. 16 can be rewritten as follows
.cndot. ( .lamda. ) = 1 ( 2 .pi. ) n + 1 .sigma. b 2 .intg. Z - 1 2
( Z T G 3 Z - 2 Z T W ) - .mu. b t y t .lamda. t + .mu. b 2 t '
.lamda. t ' .cndot. .gamma. .cndot. .gamma. ' , ( 18 )
##EQU00012##
yielding, after integration, the following partition function
Z ( .lamda. ) = G 3 - 1 .sigma. b + 1 2 W T G 3 - 1 W - .mu. b t y
t .lamda. t + .mu. b 2 t ' .lamda. t ' .cndot. .gamma. .cndot.
.gamma. ' , ( 19 ) ##EQU00013##
i.e. the final objective function is
( .lamda. ) = - 1 2 log G 3 - 1 .sigma. b 2 - 1 2 W T G 3 - 1 W +
.mu. b t y t .lamda. t - .mu. b 2 t .lamda. t ' + t ( 1 + 1 c )
.lamda. t + log ( 1 - .lamda. t c ) + t ' ( 1 + 1 c ) .lamda. t ' +
log ( 1 - .lamda. t ' c ) . ( 20 ) ##EQU00014##
[0100] The objective function I can be solved by applying similar
techniques as in the case of known labels as discussed in the
section herein entitled M Step. The difference is that matrix
G.sub.3.sup.-1 in the quadratic form of the maximum margin term has
now off-diagonal terms.
[0101] There exist many applications of method of the present
invention employing Maximum Entropy Discrimination framework
besides classification. For example MED can be applied to solve
classification of data, in general, any kind of discriminant
function and prior distributions, regression and graphical models
(T. Jebara, Machine Learning Discriminative and Generative, Kluwer
Academic Publishers) (Jebara).
[0102] The applications of the embodiments of the present invention
can be formulated as pure inductive learning problems with known
labels as well as a transductive learning problem with labeled as
well as unlabeled training examples. In the latter case, the
improvements to the transductive MED classification algorithm
described in Table 3 are applicable as well to general transductive
MED classification, transductive MED regression, transductive MED
learning of graphical models. As such, for purposes of this
disclosure and the accompanying claims, the word "classification"
may include regression or graphical models.
M Step
[0103] According to Eq. 11, the M step's objective function is
M ( .lamda. ) = - 1 2 t , t ' y t y t ' .lamda. t .lamda. t ' K ( X
t , X t ' ) - .sigma. b 2 2 ( t .lamda. t y t ) 2 - .mu. b t
.lamda. t y t + t ( 1 + 1 c ) .lamda. t + t log ( 1 - .lamda. t c )
, { .lamda. t | 0 .ltoreq. .lamda. t .ltoreq. c } , ( 21 )
##EQU00015##
whereby the Lagrange Multipliers .lamda..sub.t are determined by
maximizing J.sub.M.
[0104] Omitting the redundant constraint that .lamda..sub.t<c,
the Lagrangian for the dual problem above is
.English Pound. M ( .lamda. ) = - 1 2 t , t ' y t y t ' .lamda. t
.lamda. t ' K ( X t , X t ' ) - .sigma. b 2 2 ( t .lamda. t y t ) 2
- .mu. b t .lamda. t y t + t ( 1 + 1 c ) .lamda. t + t log ( 1 -
.lamda. t c ) , + t .delta. t .lamda. t , .A-inverted. t : 0
.ltoreq. .lamda. t .ltoreq. c , .delta. t .gtoreq. 0 , .delta. t
.lamda. t = 0. ( 22 ) ##EQU00016##
[0105] The KKT conditions, which are necessary and sufficient for
optimality, are
.differential. .English Pound. M ( .lamda. ) .differential. .lamda.
t = - t ' y t y t ' .lamda. t ' K ( X t , X t ' ) - .sigma. b 2 y t
t ' .lamda. t ' y t ' - .mu. b y t + ( 1 + 1 c ) - 1 c - .lamda. t
+ .delta. t = - t ' y t y t ' .lamda. t ' K ( X t , X t ' ) -
.sigma. b 2 y t t ' .lamda. t ' y t ' - .mu. b y t + y t y t ( 1 +
1 c ) - y t y t ( c - .lamda. t ) + .delta. t = y t ( - t ' y t '
.lamda. t ' K ( X t , X t ' ) - .sigma. b 2 t ' .lamda. t ' y t ' -
.mu. b + 1 y t ( 1 + 1 c ) - 1 y t ( c - .lamda. t ) ) + .delta. t
= y t ( - F t - .sigma. b 2 t ' .lamda. t ' y t ' - .mu. b ) +
.delta. t = 0 .A-inverted. t : 0 .delta. t .gtoreq. 0 , .delta. t
.lamda. t = 0 ( 23 ) ##EQU00017##
whereby F is
F t = t ' y t ' .lamda. t ' K ( X t , X t ' ) + 1 y t ( 1 + 1 c ) -
1 y t ( c - .lamda. t ) . ( 24 ) ##EQU00018##
[0106] At optimum, the basis equals the expected bias
v=.sigma..sub.b.sup.2.SIGMA..sub.t.lamda..sub.ty.sub.t+.mu..sub.b
yielding
y.sub.t(-F.sub.t-b)+.delta..sub.t=0 (25)
[0107] These equations can be summarized by considering two cases
using the .delta..sub.t.lamda..sub.t=0 constraint. The first case
for all .lamda..sub.t=0, and second for all
0<.lamda..sub.t<c. There is no need for the third case as
described in S. Keerthi, S. Shevade, C. Bhattacharyya, and K.
Murthy, Improvements to platt's smo algorithm for sym classifier
design, 1999 (Keerthi), applied to the SVM algorithm; the potential
function in this formulation maintains that
.lamda..sub.t.noteq.c.
.lamda..sub.t=0,.delta..sub.t.gtoreq.0(F.sub.t+b)y.sub.t.gtoreq.0
(26)
0<.lamda..sub.t<c,.delta..sub.t=0(F.sub.t+b)=0 (27)
[0108] Until the optimum is reached violations of these conditions
for some data point t will be present. Namely, F.sub.t.noteq.-b
when .lamda..sub.t is nonzero or F.sub.ty.sub.t<-by.sub.t when
it is zero. Unfortunately, calculating b is impossible without the
optimum .lamda..sub.t's. A good solution to this is borrowed from
Keerthi, referenced herein again, by constructing the following
three sets.
I.sub.0={t:0<.lamda..sub.t<c} (28)
I.sub.1={t:y.sub.t>0,.lamda..sub.t=0} (29)
I.sub.4={t:y.sub.t<0,.lamda..sub.t=0} (30)
[0109] Using these sets we can define the most extreme violations
of the optimality conditions using the following definitions. The
elements in I.sub.0 are violations whenever they are not equal to
-b, therefore, the largest and smallest F.sub.t from I.sub.0 are
candidates for being violations. The elements in I.sub.1 are
violations when F.sub.t<-b so the smallest element from I.sub.1
is the most extreme violation if one exists. Lastly, the elements
in I.sub.4 are violations when F.sub.t>-b, which makes the
largest elements from I.sub.4 violation candidates. Therefore, -b
is bounded by the min and max over these sets as shown below.
- b up = F t min { F t : t .di-elect cons. I 0 I 1 } ( 31 ) - b low
= F t max { F t : t .di-elect cons. I 0 I 4 } ( 32 )
##EQU00019##
[0110] Due to the fact that at optimum -b.sub.up and -b.sub.low
must be equal, namely -b, then reducing the gap between -b.sub.up
and -b.sub.low will push the training algorithm to convergence.
Additionally, the gap can also be measured as a way to determine
numerical convergence.
[0111] As previously stated, the value of b=b is not known until
convergence. The method of this alternate embodiment differs in
that only one example can be optimized at a time. Therefore the
training heuristic is to alternate between the examples in I.sub.0
and all of the examples every other time.
E Step
[0112] The E step's objective function of Eq. 12 is
E ( .lamda. ) = t ( 1 + 1 c ) .lamda. t + log ( 1 + .lamda. t c ) -
t log y t = .+-. 1 p 0 , t ( y t ) y t .lamda. t s t { .lamda. t |
0 .ltoreq. .lamda. t .gtoreq. c } , ( 33 ) ##EQU00020##
whereby s.sub.t is the t-th datapoint's classification score
determined in the previous M step. The Lagrange Multipliers
.lamda..sub.t are determined by maximizing I.sub.E.
[0113] Omitting the redundant constraint that .lamda..sub.t<c,
the Lagrangian for the dual problem above is:
.English Pound. E ( .lamda. ) = t ( 1 + 1 c ) .lamda. t + log ( 1 -
.lamda. t c ) - t log y t = .+-. 1 p 0 , t ( y t ) y t .lamda. t s
t + t .delta. t .lamda. t , .A-inverted. t : 0 .ltoreq. .lamda. t
.ltoreq. c , .delta. t .gtoreq. 0 , .delta. t .lamda. t = 0 ( 34 )
##EQU00021##
[0114] The KKT conditions, which are necessary and sufficient for
optimality, are
.differential. L ( .lamda. ) .differential. .lamda. t = ( 1 - 1 c )
- 1 c - .lamda. t - s t P 0 , t ( + 1 ) .lamda. t s t - P 0 , t ( -
1 ) - .lamda. t s t P 0 , t ( + 1 ) .lamda. t s t + P 0 , t ( - 1 )
- .lamda. t s t + .delta. t = 0. ( 35 ) ##EQU00022##
[0115] Solving for the Lagrange5 multipliers by optimizing the KKT
conditions can be done in one pass over the exampled since they
factorize over the examples.
[0116] For labeled examples the expected label y.sub.t is one with
P.sub.0,t(y.sub.t)=1 and P.sub.0,t(-y.sub.t)=0 reducing the KKT
conditions to
.differential. L E ( .lamda. ) .differential. .lamda. t = ( 1 - 1 c
) - 1 c - .lamda. t - s t y t + .delta. t = 0 ( 36 )
##EQU00023##
and yielding as solutions for the Lagrange Multipliers of labeled
examples
.lamda. t = c - 1 - c y t s t ( 1 - 1 c ) y t s t . ( 37 )
##EQU00024##
[0117] For unlabeled examples, Eq. 35 cannot be solved
analytically, but has to be determined by applying e.g. a linear
search for each unlabeled example's Lagrange Multiplier that
satisfies Eq. 35.
[0118] The following are several non-limiting examples that are
enabled by the techniques illustrated above, derivations or
variations thereof, and other techniques known in the art. Each
example includes the preferred operations, along with optional
operations or parameters that may be implemented in the basic
preferred methodology.
[0119] In one embodiment, as presented in FIG. 10, labeled data
points are received at step 1002, where each of the labeled data
points has at least one label which indicates whether the data
point is a training example for data points for being included in a
designated category or a training example for data points being
excluded from a designated category. In addition, unlabeled data
points are received at step 1004, as well as at least one
predetermined cost factor of the labeled data points and unlabeled
data points. The data points may contain any medium, e.g. words,
images, sounds, etc. Prior probability information of labeled and
unlabeled data points may also be received. Also, the label of the
included training example may be mapped to a first numeric value,
e.g. +1, etc., and the label of the excluded training example may
be mapped to a second numeric value, e.g. -1, etc. In addition, the
labeled data points, unlabeled data points, input data points, and
at least one predetermined cost factor of the labeled data points
and unlabeled data points may be stored in a memory of a
computer.
[0120] Further, at step 1006 a transductive MED classifier is
trained through iterative calculation using said at least one cost
factor and the labeled data points and the unlabeled data points as
training examples. For each iteration of the calculations, the
unlabeled data point cost factor is adjusted as a function of an
expected label value, e.g. the absolute value of the expected label
of a data point, etc., and a data point label prior probability is
adjusted according to an estimate of a data point class membership
probability, thereby ensuring stability. Also, the transductive
classifier may learn using prior probability information of the
labeled and unlabeled data, which further improves stability. The
iterative step of training a transductive classifier may be
repeated until the convergence of data values is reached, e.g. when
the change of the decision function of the transductive classifier
falls below a predetermined threshold value, when the change of the
determined expected label value falls below a predetermined
threshold value, etc.
[0121] Additionally, in step 1008 the trained classifier is applied
to classify at least one of the unlabeled data points, the labeled
data points, and input data points. Input data points may be
received before or after the classifier is trained, or may not be
received at all. Also, the decision function that minimizes the KL
divergence to the prior probability distribution of the decision
function parameters given the included and excluded training
examples may be determined utilizing the labeled as well as the
unlabeled data points as learning examples according to their
expected label. Alternatively, the decision function may be
determined with minimal KL divergence using a multinomial
distribution for the decision function parameters.
[0122] In step 1010 a classification of the classified data points,
or a derivative thereof, is output to at least one of a user,
another system, and another process. The system may be remote or
local. Examples of the derivative of the classification may be, but
are not limited to, the classified data points themselves, a
representation or identifier of the classified data points or host
file/document, etc.
[0123] In another embodiment, computer executable program code is
deployed to and executed on a computer system. This program code
comprises instructions for accessing stored labeled data points in
a memory of a computer, where each of said labeled data points has
at least one label indicating whether the data point is a training
example for data points for being included in a designated category
or a training example for data points being excluded from a
designated category. In addition, the computer code comprises
instructions for accessing unlabeled data points from a memory of a
computer as well as accessing at least one predetermined cost
factor of the labeled data points and unlabeled data points from a
memory of a computer. Prior probability information of labeled and
unlabeled data points stored in a memory of a computer may also be
accessed. Also, the label of the included training example may be
mapped to a first numeric value, e.g. +1, etc., and the label of
the excluded training example may be mapped to a second numeric
value, e.g. -1, etc.
[0124] Further, the program code comprises instructions for
training a transductive classifier through iterative calculation,
using the at least one stored cost factor and stored labeled data
points and stored unlabeled data points as training examples. Also,
for each iteration of the calculation, the unlabeled data point
cost factor is adjusted as a function of the expected label value
of the data point, e.g. the absolute value of the expected label of
a data point. Also, for each iteration, the prior probability
information may be adjusted according to an estimate of a data
point class membership probability. The iterative step of training
a transductive classifier may be repeated until the convergence of
data values is reached, e.g. when the change of the decision
function of the transductive classifier falls below a predetermined
threshold value, when the change of the determined expected label
value falls below a predetermined threshold value, etc.
[0125] Additionally, the program code comprises instructions for
applying the trained classifier to classify at least one of the
unlabeled data points, the labeled data points, and input data
points, as well as instructions for outputting a classification of
the classified data points, or derivative thereof, to at least one
of a user, another system, and another process. Also, the decision
function that minimizes the KL divergence to the prior probability
distribution of the decision function parameters given the included
and excluded training examples may be determined utilizing the
labeled as well as the unlabeled data as learning examples
according to their expected label.
[0126] In yet another embodiment, a data processing apparatus
comprises at least one memory for storing: (i) labeled data points,
wherein each of said labeled data points have at least one label
indicating whether the data point is a training example for data
points being included in a designated category or a training
example for data points being excluded from a designated category;
(ii) unlabeled data points, and (iii) at least one predetermined
cost factor of the labeled data points and unlabeled data points.
The memory may also store prior probability information of labeled
and unlabeled data points. Also, the label of the included training
example may be mapped to a first numeric value, e.g. +1, etc., and
the label of the excluded training example may be mapped to a
second numeric value, e.g. -1, etc.
[0127] In addition, the data processing apparatus comprises a
transductive classifier trainer to iteratively teach the
transductive classifier using transductive Maximum Entropy
Discrimination (MED) using the at least one stored cost factor and
stored labeled data points and stored unlabeled data points as
training examples. Further, at each iteration of the MED
calculation the cost factor of the unlabeled data point is adjusted
as a function of the expected label value of the data point, e.g.
the absolute value of the expected label of a data point, etc.
Also, at each iteration of the MED calculation, the prior
probability information may be adjusted according to an estimate of
a data point class membership probability. The apparatus may
further comprise a means for determining the convergence of data
values, e.g. when the change of the decision function of the
transductive classifier calculation falls below a predetermined
threshold value, when the change of the determined expected label
values falls below a predetermined threshold value, etc., and
terminating calculations upon determination of convergence.
[0128] In addition, a trained classifier is used to classify at
least one of the unlabeled data points, the labeled data points,
and input data points. Further, the decision function that
minimizes the KL divergence to the prior probability distribution
of the decision function parameters given the included and excluded
training examples may be determined by a processor utilizing the
labeled as well as the unlabeled data as learning examples
according to their expected label. Also, a classification of the
classified data points, or derivative thereof, is output to at
least one of a user, another system, and another process.
[0129] In a further embodiment, an article of manufacture comprises
a program storage medium readable by a computer, where the medium
tangibly embodies one or more programs of instructions executable
by a computer to perform a method of data classification. In use,
labeled data points are received, where each of the labeled data
points has at least one label which indicates whether the data
point is a training example for data points for being included in a
designated category or a training example for data points being
excluded from a designated category. In addition, unlabeled data
points are received, as well as at least one predetermined cost
factor of the labeled data points and unlabeled data points. Prior
probability information of labeled and unlabeled data points may
also be stored in a memory of a computer. Also, the label of the
included training example may be mapped to a first numeric value,
e.g. +1, etc., and the label of the excluded training example may
be mapped to a second numeric value, e.g. -1, etc.
[0130] Further, a transductive classifier is trained with iterative
Maximum Entropy Discrimination (MED) calculation using the at least
one stored cost factor and the stored labeled data points and the
unlabeled data points as training examples. At each iteration of
the MED calculation, the unlabeled data point cost factor is
adjusted as a function of an expected label value of the data
point, e.g. the absolute value of the expected label of a data
point, etc. Also, at each iteration of the MED calculation, the
prior probability information may be adjusted according to an
estimate of a data point class membership probability. The
iterative step of training a transductive classifier may be
repeated until the convergence of data values is reached, e.g. when
the change of the decision function of the transductive classifier
falls below a predetermined threshold value, when the change of the
determined expected label value falls below a predetermined
threshold value, etc.
[0131] Additionally, input data points are accessed from the memory
of a computer, and the trained classifier is applied to classify at
least one of the unlabeled data points, the labeled data points,
and input data points. Also, the decision function that minimizes
the KL divergence to the prior probability distribution of the
decision function parameters given the included and excluded
training examples may be determined utilizing the labeled as well
as the unlabeled data as learning examples according to their
expected label. Further, a classification of the classified data
points, or a derivative thereof, is output to at least one of a
user, another system, and another process.
[0132] In yet another embodiment, a method for classification of
unlabeled data in a computer-based system is presented. In use,
labeled data points are received, each of said labeled data points
having at least one label indicating whether the data point is a
training example for data points for being included in a designated
category or a training example for data points being excluded from
a designated category.
[0133] Additionally, labeled and unlabeled data points are
received, as are prior label probability information of labeled
data points and unlabeled data points. Further, at least one
predetermined cost factor of the labeled data points and unlabeled
data points is received.
[0134] Further, the expected labels for each labeled and unlabeled
data point are determined according to the label prior probability
of the data point. The following substeps are repeated until
substantial convergence of data values: [0135] generating a scaled
cost value for each unlabeled data point proportional to the
absolute value of the data point's expected label; [0136] training
a Maximum Entropy Discrimination (MED) classifier by determining
the decision function that minimizes the KL divergence to the prior
probability distribution of the decision function parameters given
the included training and excluded training examples utilizing the
labeled as well as the unlabeled data as training examples
according to their expected label; [0137] determining the
classification scores of the labeled and unlabeled data points
using the trained classifier; [0138] calibrating the output of the
trained classifier to class membership probability; [0139] updating
the label prior probabilities of the unlabeled data points
according to the determined class membership probabilities; [0140]
determining the label and margin probability distributions using
Maximum Entropy Discrimination (MED) using the updated label prior
probabilities and the previously determined classification scores;
[0141] computing new expected labels using the previously
determined label probability distribution; and [0142] updating
expected labels for each data point by interpolating the new
expected labels with the expected label of previous iteration.
[0143] Also, a classification of the input data points, or
derivative thereof, is output to at least one of a user, another
system, and another process.
[0144] Convergence may be reached when the change of the decision
function falls below a predetermined threshold value. Additionally,
convergence may also be reached when the change of the determined
expected label value falls below a predetermined threshold value.
Further, the label of the included training example may have any
value, for example, a value of +1, and the label of the excluded
training example may have any value, for example, a value of
-1.
[0145] In one embodiment of the present invention, a method for
classifying documents is presented in FIG. 11. In use, at least one
seed document having a known confidence level is received in step
1100, as well as unlabeled documents and at least one predetermined
cost factor. The seed document and other items may be received from
a memory of a computer, from a user, from a network connection,
etc., and may be received after a request from the system
performing the method. The at least one seed document may have a
label indicative of whether the document is included in a
designated category, may contain a list of keywords, or have any
other attribute that may assist in classifying documents. Further,
in step 1102 a transductive classifier is trained through iterative
calculation using the at least one predetermined cost factor, the
at least one seed document, and the unlabeled documents, wherein
for each iteration of the calculations the cost factor is adjusted
as a function of an expected label value. A data point label prior
probability for the labeled and unlabeled documents may also be
received, wherein for each iteration of the calculations the data
point label prior probability may be adjusted according to an
estimate of a data point class membership probability.
[0146] Additionally, after at least some of the iterations, in step
1104 confidence scores are stored for the unlabeled documents, and
identifiers of the unlabeled documents having the highest
confidence scores are output in step 1106 to at least one of a
user, another system, and another process. The identifiers may be
electronic copies of the document themselves, portions thereof,
titles thereof, names thereof, file names thereof, pointers to the
documents, etc. Also, confidence scores may be stored after each of
the iterations, wherein an identifier of the unlabeled document
having the highest confidence score after each iteration is
output.
[0147] One embodiment of the present invention is capable of
discovering patterns that link the initial document to the
remaining documents. The task of discovery is one area where this
pattern discovery proves particularly valuable. For instance, in
pre-trial legal discovery, a large amount of documents have to be
researched with regard to possible connections to the lawsuit at
hand.
[0148] The ultimate goal is to find the "smoking gun." In another
example, a common task for inventors, patent examiners, as well as
patent lawyers is to evaluate the novelty of a technology through
prior art search. In particular the task is to search all published
patents and other publications and find documents within this set
that might be related to the specific technology that is examined
with regard to its novelty.
[0149] The task of discovery involves finding a document or a set
of documents within a set of data. Given an initial document or
concept, a user may want to discover documents that are related to
the initial document or concept. However, the notion of
relationship between the initial document or concept and the target
documents, i.e. the documents that are to be discovered, is only
well understood after the discovery has taken place. By learning
from labeled and unlabeled documents, concepts, etc., the present
invention can learn patterns and relationships between the initial
document or documents and the target documents.
[0150] In another embodiment of the present invention, a method for
analyzing documents associated with legal discovery is presented in
FIG. 12. In use, documents associated with a legal matter are
received in step 1200. Such documents may include electronic copies
of the document themselves, portions thereof, titles thereof, names
thereof, file names thereof, pointers to the documents, etc.
Additionally, a document classification technique is performed on
the documents in step 1202. Further, identifiers of at least some
of the documents are output in step 1204 based on the
classification thereof. As an option, a representation of links
between the documents may also be output.
[0151] The document classification technique may include any type
of process, e.g. a transductive process, etc. For example, any
inductive or transductive technique described above may be used. In
a preferred approach, a transductive classifier is trained through
iterative calculation using at least one predetermined cost factor,
at least one seed document, and the documents associated with the
legal matter. For each iteration of the calculations the cost
factor is preferably adjusted as a function of an expected label
value, and the trained classifier is used to classify the received
documents. This process may further comprise receiving a data point
label prior probability for the labeled and unlabeled documents,
wherein for each iteration of the calculations the data point label
prior probability is adjusted according to an estimate of a data
point class membership probability. Additionally, the document
classification technique may include one or more of a support
vector machine process and a maximum entropy discrimination
process.
[0152] In yet another embodiment, a method for analyzing prior art
documents is presented in FIG. 13. In use, a classifier is trained
based on a search query in step 1300. A plurality of prior art
documents are accessed in step 1302. Such prior art may include any
information that has been made available to the public in any form
before a given date. Such prior art may also or alternatively
include any information that has not been made available to the
public in any form before a given date. Illustrative prior art
documents may be any type of documents, e.g. publications of a
patent office, data retrieved from a database, a collection of
prior art, portions of a website, etc. Also, a document
classification technique is performed on at least some of the prior
art documents in step 1304 using the classifier, and identifiers of
at least some of the prior art documents are output in step 1306
based on the classification thereof. The document classification
technique may include one or more of any process, including a
support vector machine process, a maximum entropy discrimination
process, or any inductive or transductive technique described
above. Also or alternatively, a representation of links between the
documents may also be output. In yet another embodiment, a
relevance score of at least some of the prior art documents is
output based on the classification thereof.
[0153] The search query may include at least a portion of a patent
disclosure. Illustrative patent disclosures include a disclosure
created by an inventor summarizing the invention, a provisional
patent application, a nonprovisional patent application, a foreign
patent or patent application, etc.
[0154] In one preferred approach, the search query includes at
least a portion of a claim from a patent or patent application. In
another approach, the search query includes at least a portion of
an abstract of a patent or patent application. In a further
approach, the search query includes at least a portion of a summary
from a patent or patent application.
[0155] FIG. 27 illustrates a method for matching documents to
claims. In step 2700, a classifier is trained based on at least one
claim of a patent or patent application. Thus, one or more claims,
or a portion thereof, may be used to train the classifier. In step
2702, a plurality of documents are accessed. Such documents may
include prior art documents, documents describing potentially
infringing or anticipating products, etc. In step 2704, a document
classification technique is performed on at least some of the
documents using the classifier. In step 2706, identifiers of at
least some of the documents are output based on the classification
thereof. A relevance score of at least some of the documents may
also be output based on the classification thereof.
[0156] An embodiment of the present invention may be used for the
classification of patent applications. In the United States, for
example, patents and patent applications are currently classified
by subject matter using the United States Patent Classification
(USPC) system. This task is currently performed manually, and
therefore is very expensive and time consuming. Such manual
classification is also subject to human errors. Compounding the
complexity of such a task is that the patent or patent application
may be classified into multiple classes.
[0157] FIG. 28 depicts a method for classifying a patent
application according to one embodiment. In step 2800, a classifier
is trained based on a plurality of documents known to be in a
particular patent classification. Such documents may typically be
patents and patent applications (or portions thereof), but could
also be summary sheets describing target subject matter of the
particular patent classification. In step 2802, at least a portion
of a patent or patent application is received. The portion may
include the claims, summary, abstract, specification, title, etc.
In step 2804, a document classification technique is performed on
the at least the portion of the patent or patent application using
the classifier. In step 2806, a classification of the patent or
patent application is output. As an option, a user may manually
verify the classification of some or all of the patent
applications.
[0158] The document classification technique is preferably a yes/no
classification technique. In other words, if the probability that
the document is in the proper class is above a threshold, the
decision is yes, the document belongs in this class. If the
probability that the documents is in the proper class is below a
threshold, the decision is no, the document does not belong in this
class.
[0159] FIG. 29 depicts yet another method for classifying a patent
application. In step 2900, a document classification technique is
performed on at least the portion of a patent or patent application
using a classifier that was trained based on at least one document
associated with a particular patent classification. Again, the
document classification technique is preferably a yes/no
classification technique. In step 2902, a classification of the
patent or patent application is output.
[0160] In either of the methods shown in FIGS. 28 and 29, the
respective method may be repeated using a different classifier that
was trained based on a plurality of documents known to be in a
different patent classification.
[0161] Officially, classification of a patent should be based on
the claims. However, it may also be desirable to perform matching
between (any IP related content) and (any IP related content). As
an example, one approach uses the Description of a patent to train,
and classify an application based on its Claims. Another approach
uses the Description and Claims to train, and classify based on the
Abstract. In particularly preferred approaches, whatever portion of
a patent or application is used to train, that same type of content
is used when classifying, i.e., if the system is trained on claims,
the classification is based on claims.
[0162] The document classification technique may include any type
of process, e.g. a transductive process, etc. For example, any
inductive or transductive technique described above may be used. In
a preferred approach, the classifier may be a transductive
classifier, and the transductive classifier may be trained through
iterative calculation using at least one predetermined cost factor,
at least one seed document, and the prior art documents, wherein
for each iteration of the calculations the cost factor is adjusted
as a function of an expected label value, and the trained
classifier may be used to classify the prior art documents. A data
point label prior probability for the seed document and prior art
documents may also be received, wherein for each iteration of the
calculations the data point label prior probability may be adjusted
according to an estimate of a data point class membership
probability. The seed document may be any document, e.g.
publications of a patent office, data retrieved from a database, a
collection of prior art, a website, a patent disclosure, etc.
[0163] In one approach, FIG. 14 describes one embodiment of the
present invention. In step 1401, a set of data is read. The
discovery of documents within this set that are relevant to the
user is desired. In step 1402 an initial seed document or documents
are labeled. The documents may be any type of documents, e.g.
publications of a patent office, data retrieved from a database, a
collection of prior art, a website, etc. It is also possible to
seed the transduction process with a string of different key words
or a document provided by the user. In step 1406 training a
transductive classifier is trained using the labeled data as well
as the set of unlabeled data in the given set. At each label
induction step during the iterative transduction process the
confidence scores determined during label induction are stored.
Once training is finished, the documents that achieved high
confidence scores at the label induction steps are displayed in
step 1408 for the user. These documents with high confidence scores
represent documents relevant to the user for purposes of discovery.
The display may be in chronological order of the label induction
steps starting with the initial seed document to the final set of
documents discovered at the last label induction step.
[0164] Another embodiment of the present invention involves data
cleanup and accurate classification, for example in conjunction
with the automation of business processes. The cleanup and
classification technique may include any type of process, e.g. a
transductive process, etc. For example, any inductive or
transductive technique described above may be used. In a preferred
approach, the keys of the entries in the database are utilized as
labels associated with some confidence level according to the
expected cleanliness of the database. The labels together with the
associated confidence level, i.e. the expected labels, are then
used to train a transductive classifier that corrects the labels
(keys) in order to achieve a more consistent organization of the
data in the database. For example, invoices have to be first
classified according to the company or person that originated the
invoice in order to enable automatic data extraction, e.g. the
determination of total dollar amount, purchase order number,
product amount, shipping address, etc. Commonly, training examples
are needed to set up an automatic classification system. However,
training examples provided by the customer often contain
misclassified documents or other noise--e.g. fax cover sheets--that
have to be identified and removed prior to training the automatic
classification system in order to obtain accurate classification.
In another example, in the area of patient records, it is useful to
detect inconsistencies between the report written by the physician
and the diagnosis.
[0165] In another example, it is known that the Patent Office
undergoes a continuous reclassification process, in which they (1)
evaluate an existing branch of their taxonomy for confusion, (2)
re-structure that taxonomy to evenly distributed overly congested
nodes, and (3) reclassify existing patents into the new structure.
The transductive learning methods presented herein may be used by
the Patent Office, and the companies they outsource to do this
work, to reevaluate their taxonomy, and assist them in (1) build a
new taxonomy for a given main classification, and (2) reclassifying
existing patents.
[0166] Transduction learns from labeled and unlabeled data, whereby
the transition from labeled to unlabeled data is fluent. At one end
of the spectrum are labeled data with perfect prior knowledge, i.e.
the given labels are correct with no exceptions. At the other end
are unlabeled data where no prior knowledge is given. Organized
data with some level of noise constitute mislabeled data and are
located somewhere on the spectrum between these two extremes: The
labels given by the organization of the data can be trusted to be
correct to some extent but not fully. Accordingly, transduction can
be utilized to clean up the existing organization of data by
assuming a certain level of mistakes within the given organization
of the data and interpreting these as uncertainties in the prior
knowledge of label assignments.
[0167] In one embodiment, a method for cleaning up data is
presented in FIG. 15. In use, a plurality of labeled data items are
received in step 1500, and subsets of the data items for each of a
plurality of categories are selected in step 1502. Additionally, an
uncertainty for the data items in each subset is set in step 1504
to about zero, and an uncertainty for the data items not in the
subsets is set in step 1506 to a predefined value that is not about
zero. Further, a transductive classifier is trained in step 1508
through iterative calculation using the uncertainties, the data
items in the subsets, and the data items not in the subsets as
training examples, and the trained classifier is applied to each of
the labeled data items in step 1510 to classify each of the data
items. Also, a classification of the input data items, or
derivative thereof, is output in step 1512 to at least one of a
user, another system, and another process.
[0168] Further, the subsets may be selected at random and may be
selected and verified by a user. The label of at least some of the
data items may be changed based on the classification. Also,
identifiers of data items having a confidence level below a
predefined threshold after classification thereof may be output to
a user. The identifiers may be electronic copies of the document
themselves, portions thereof, titles thereof, names thereof, file
names thereof, pointers to the documents, etc.
[0169] In one embodiment of the present invention, as illustrated
in FIG. 16, two choices to start a cleanup process are presented to
the user at step 1600. One choice is fully automatic cleanup at
step 1602, where for each concept or category a specified number of
documents are randomly selected and assumed to be correctly
organized. Alternatively, at step 1604 a number of documents can be
flagged for manual review and verification that one or more label
assignments for each concept or category is being correctly
organized. An estimate of the noise level in the data is received
at step 1606. The transductive classifier is trained in step 1610
using the verified (manually verified or randomly selected) data
and the unverified data in step 1608. Once training is finished the
documents are reorganized according to the new labels. Documents
with low confidence levels in their label assignments below a
specified threshold are displayed for the user for manual review in
step 1612. Documents with confidence levels in their label
assignments above a specified threshold are automatically corrected
according to transductive label assignments in step 1614.
[0170] In another embodiment, a method for managing medical records
is presented in FIG. 17. In use, a classifier is trained based on a
medical diagnosis in step 1700, and a plurality of medical records
is accessed in step 1702. Additionally, a document classification
technique is performed on the medical records in step 1704 using
the classifier, and an identifier of at least one of the medical
records having a low probability of being associated with the
medical diagnosis is output in step 1706. The document
classification technique may include any type of process, e.g. a
transductive process, etc., and may include one or more of any
inductive or transductive technique described above, including a
support vector machine process, a maximum entropy discrimination
process, etc.
[0171] In one embodiment, the classifier may be a transductive
classifier, and the transductive classifier may be trained through
iterative calculation using at least one predetermined cost factor,
at least one seed document, and the medical records, wherein for
each iteration of the calculations the cost factor is adjusted as a
function of an expected label value, and the trained classifier may
be used to classify the medical records. A data point label prior
probability for the seed document and medical records may also be
received, wherein for each iteration of the calculations the data
point label prior probability may be adjusted according to an
estimate of a data point class membership probability.
[0172] Another embodiment of the present invention accounts for
dynamic, shifting classification concepts. For example, in forms
processing applications documents are classified using the layout
information and/or the content information of the documents to
classify the documents for further processing. In many applications
the documents are not static but evolve over time. For example the
content and/or layout of a document may change owing to new
legislation. Transductive classification adapts to these changes
automatically yielding the same or comparable classification
accuracy despite the drifting classification concepts. This is in
contrast to rule based systems or inductive classification methods
that, without manually adjustments, will start to suffer in
classification accuracy owing to the concept drift. One example of
this is invoice processing, which traditionally involves inductive
learning, or rule-based systems are used that utilize invoice
layout. Under these traditional systems, if a change in the layout
occurs the systems have to be manually reconfigured by either
labeling new training data or by determining new rules. However,
the use of transduction makes the manual reconfiguration
unnecessary by automatically adapting to the small changes in
layout of the invoices. In another example, transductive
classification may be applied to the analysis of customer
complaints in order to monitor the changing nature of such
complaints. For example, a company can automatically link product
changes with customer complaints.
[0173] Transduction may also be used in the classification of news
articles. For example, news articles on the war on terror starting
with articles about the terrorist attacks on Sep. 11, 2001 over the
war in Afghanistan to news stories about the situation in today's
Iraq can be automatically identified using transduction.
[0174] In yet another example, the classification of organisms
(alpha taxonomy) can change over time through evolution by creating
new species of organisms and other species becoming extinct. This
and other principles of a classification schema or taxonomy can be
dynamic, with classification concepts shifting or changing over
time.
[0175] By using the incoming data that have to be classified as
unlabeled data, transduction can recognize shifting classification
concepts, and therefore dynamically adapt to the evolving
classification schema. For example, FIG. 18 shows an embodiment of
the invention using transduction given drifting classification
concepts. Document set D.sub.i enters the system at time t.sub.i,
as shown in step 1802. At step 1804 a transductive classifier
C.sub.i is trained using labeled data and the unlabeled data
accumulated so far, and in step 1806 the documents in set D.sub.i
are classified. If the manual mode is used, documents with a
confidence level below a user supplied threshold as determined in
step 1808 are presented to the user for manual review in step 1810.
As shown in step 1812, in the automatic mode a document with a
confidence level triggers the creation of a new category that is
added to the system, and the document is then assigned to the new
category. Documents with a confidence level above the chosen
threshold are classified into the current categories 1 to N in
steps 1820A-B. All documents in the current categories that have
been classified prior to step t.sub.i into the current categories
are reclassified by the classifier C.sub.i in step 1822, and all
documents that are no longer classified into the previously
assigned categories are moved to new categories in steps 1824 and
1826.
[0176] In yet another embodiment, a method for adapting to a shift
in document content is presented in FIG. 19. Document content may
include, but is not limited to, graphical content, textual content,
layout, numbering, etc. Examples of shift may include temporal
shift, style shift (where 2 or more people work on one or more
documents), shift in process applied, shift in layout, etc. In step
1900, at least one labeled seed document is received, as well as
unlabeled documents and at least one predetermined cost factor. The
documents may include, but are not limited to, customer complaints,
invoices, form documents, receipts, etc. Additionally, a
transductive classifier is trained in step 1902 using the at least
one predetermined cost factor, the at least one seed document, and
the unlabeled documents. Also, in step 1904 the unlabeled documents
having a confidence level above a predefined threshold are
classified into a plurality of categories using the classifier, and
at least some of the categorized documents are reclassified in step
1906 into the categories using the classifier. Further, identifiers
of the categorized documents are output in step 1908 to at least
one of a user, another system, and another process. The identifiers
may be electronic copies of the document themselves, portions
thereof, titles thereof, names thereof, file names thereof,
pointers to the documents, etc. Further, product changes may be
linked with customer complaints, etc.
[0177] In addition, an unlabeled document having a confidence level
below the predefined threshold may be moved into one or more new
categories. Also, the transductive classifier may be trained
through iterative calculation using at least one predetermined cost
factor, the at least one seed document, and the unlabeled
documents, wherein for each iteration of the calculations the cost
factor may be adjusted as a function of an expected label value,
and using the trained classifier to classify the unlabeled
documents. Further, a data point label prior probability for the
seed document and unlabeled documents may be received, wherein for
each iteration of the calculations the data point label prior
probability may be adjusted according to an estimate of a data
point class membership probability.
[0178] In another embodiment, a method for adapting a patent
classification to a shift in document content is presented in FIG.
20. In step 2000, at least one labeled seed document is received,
as well as unlabeled documents. The unlabeled documents may include
any types of documents, e.g. patent applications, legal filings,
information disclosure forms, document amendments, etc. The seed
document(s) may include patent(s), patent application(s), etc. A
transductive classifier is trained in step 2002 using the at least
one seed document and the unlabeled documents, and the unlabeled
documents having a confidence level above a predefined threshold
are classified into a plurality of existing categories using the
classifier. The classifier may be any type of classifier, e.g. a
transductive classifier, etc., and the document classification
technique may be any technique, e.g. a support vector machine
process, a maximum entropy discrimination process, etc. For
example, any inductive or transductive technique described above
may be used.
[0179] Also, in step 2004 the unlabeled documents having a
confidence level below the predefined threshold are classified into
at least one new category using the classifier, and at least some
of the categorized documents are reclassified in step 2006 into the
existing categories and the at least one new category using the
classifier. Further, identifiers of the categorized documents are
output in step 2008 to at least one of a user, another system, and
another process. Also, the transductive classifier may be trained
through iterative calculation using at least one predetermined cost
factor, the search query, and the documents, wherein for each
iteration of the calculations the cost factor may be adjusted as a
function of an expected label value, and the trained classifier may
be used to classify the documents. Further, a data point label
prior probability for the search query and documents may be
received, wherein for each iteration of the calculations the data
point label prior probability is adjusted according to an estimate
of a data point class membership probability.
[0180] Yet another embodiment of the present invention accounts for
document drift in the field of document separation. One use case
for Document separation involves the processing of mortgage
documents. Loan folders consisting of a sequence of different loan
documents, e.g. loan applications, approvals, requests, amounts,
etc. are scanned and the different documents within the sequence of
images have to be determined before further processing. The
documents used are not static but can change over time. For
example, tax forms used within a loan folder can change over time
owing to legislation changes.
[0181] Document separation solves the problem of finding document
or subdocument boundaries in a sequence of images. Common examples
that produce a sequence of images are digital scanners or Multi
Functional Peripherals (MFPs). As in the case of classification,
transduction can be utilized in Document separation in order to
handle the drift of documents and their boundaries over time.
Static separation systems like rule based systems or systems based
on inductive learning solutions cannot adapt automatically to
drifting separation concepts. The performance of these static
separation systems degrade over time whenever a drift occurs. In
order to keep the performance on its initial level, one either has
to manually adapt the rules (in the case of a rule based system),
or has to manually label new documents and relearn the system (in
case of an inductive learning solution). Either way is time and
cost expensive. Applying transduction to Document separation allows
the development of a system that automatically adapts to the drift
in the separation concepts.
[0182] In one embodiment, a method for separating documents is
presented in FIG. 21. In step 2100, labeled data are received, and
in step 2102 a sequence of unlabeled documents is received. Such
data and documents may include legal discovery documents, office
actions, web page data, attorney-client correspondence, etc. In
addition, in step 2104 probabilistic classification rules are
adapted using transduction based on the labeled data and the
unlabeled documents, and in step 2106 weights used for document
separation are updated according to the probabilistic
classification rules. Also, in step 2108 locations of separations
in the sequence of documents are determined, and in step 2110
indicators of the determined locations of the separations in the
sequence are output to at least one of a user, another system, and
another process. The indicators may be electronic copies of the
document themselves, portions thereof, titles thereof, names
thereof, file names thereof, pointers to the documents, etc.
Further, in step 2112 the documents are flagged with codes, the
codes correlating to the indicators.
[0183] FIG. 22 shows an implementation of the classification method
and apparatus of the present invention used in association with
document separation. Automatic document separation is used for
reducing the manual effort involved in separating and identifying
documents after digital scanning. One such document separation
method and apparatus is described in U.S. Publication 2005/0134935
published Jun. 23, 2005 to Schmidtler et al, the substance of which
is incorporated herein by reference. In the aforementioned
publication, the method combined classification rules to
automatically separate sequences of pages by using inference
algorithms to reduce the most likely separation from all of the
available information, using the classifications methods described
therein. In one embodiment of the present invention as shown in
FIG. 22, the classification method of transductive MED of the
present invention is employed in document separation. More
particularly, document pages 2200 are inserted into a digital
scanner 2202 or MFP and are converted into a sequence of digital
images 2204. The document pages may be pages from any type of
document, e.g. publications of a patent office, data retrieved from
a database, a collection of prior art, a website, etc. The sequence
of digital images is input at step 2206 to dynamically adapt
probabilistic classification rules using transduction. Step 2206
utilizes the sequence of images 2204 as unlabeled data and labeled
data 2208. At step 2210 the weight in the probabilistic network is
updated and is used for automatic document separation according to
dynamically adapted classification rules. The output step 2212 is a
dynamic adaptation of automatic insertion of separation images such
that a sequence of digitized pages 2214 is interleaved with
automatic images of separator sheets 2216 at step 2212
automatically inserts the separator sheet images into the image
sequence. In one embodiment of the invention, the software
generated separator pages 2216 may also indicate the type of
document that immediately follows or proceeds the separator page
2216. The system described here automatically adapts to drifting
separation concepts of the documents that occur over time without
suffering from a decline in separation accuracy as would static
systems like rule based or inductive machine learning based
solutions. A common example for drifting separation or
classification concepts in form processing applications are, as
mentioned earlier, changes to documents owing to new
legislation.
[0184] Additionally, the system as shown in FIG. 22 may be modified
to a system as shown in FIG. 23 where the pages 2300 are inserted
into a digital scanner 2302 or MFP converted into a sequence of
digital images 2304. The sequence of digital images is input at
step 2306 to dynamically adapt probabilistic classification rules
using transduction. Step 2306 utilizes the sequence of images 2304,
as unlabeled data and labeled data 2308. Step 2310 updates weights
in the probabilistic network used for automatic document separation
according to dynamically adapted classification rules employed. In
step 2312 instead of inserting separator sheet images as described
in FIG. 18, step 2312 dynamically adapts the automated insertion of
separation information and flags the document images 2314 with a
coded description. Thus the document page images can be input into
an imaging processed database 2316 and the documents can be
accessed by the software identifiers.
[0185] Yet another embodiment of the present invention is able to
perform face recognition using transduction. As mentioned above,
the use of transduction has many advantages, for example the need
of a relatively small number of training examples, the ability to
use unlabeled examples in training, etc. By making use of the
aforementioned advantages, transductive face recognition may be
implemented for criminal detection.
[0186] For example, the Department of Homeland Security must ensure
that terrorists are not allowed onto commercial airliners. Part of
an airport's screening process may be to take a picture of each
passenger at the airport security checkpoint and attempt to
recognize that person. The system could initially be trained using
a small number of examples from the limited photographs available
of possible terrorists. There may also be more unlabeled
photographs of the same terrorist available in other
law-enforcement databases that may also be used in training. Thus,
a transductive trainer would take advantage of not only the
initially sparse data to create a functional face-recognition
system but would also use unlabeled examples from other sources to
increase performance. After processing the photograph taken at the
airport security checkpoint, the transductive system would be able
to recognize the person in question more accurately than a
comparable inductive system.
[0187] In yet another embodiment, a method for face recognition is
presented in FIG. 24. In step 2400, at least one labeled seed image
of a face is received, the seed image having a known confidence
level. The at least one seed image may have a label indicative of
whether the image is included in a designated category.
Additionally, in step 2400 unlabeled images are received, e.g. from
the police department, government agency, lost child database,
airport security, or any other location, and at least one
predetermined cost factor are received. Also, in step 2402 a
transductive classifier is trained through iterative calculation
using the at least one predetermined cost factor, the at least one
seed image, and the unlabeled images, wherein for each iteration of
the calculations the cost factor is adjusted as a function of an
expected label value. After at least some of the iterations, in
step 2404 confidence scores are stored for the unlabeled seed
images.
[0188] Further, in step 2406 identifiers of the unlabeled documents
having the highest confidence scores are output to at least one of
a user, another system, and another process. The identifiers may be
electronic copies of the document themselves, portions thereof,
titles thereof, names thereof, file names thereof, pointers to the
documents, etc. Also, confidence scores may be stored after each of
the iterations, wherein an identifier of the unlabeled images
having the highest confidence score after each iteration is output.
Additionally, a data point label prior probability for the labeled
and unlabeled image may be received, wherein for each iteration of
the calculations the data point label prior probability may be
adjusted according to an estimate of a data point class membership
probability. Further, a third unlabeled image of a face, e.g., from
the above airport security example, may be received, the third
unlabeled image may be compared to at least some of the images
having the highest confidence scores, and an identifier of the
third unlabeled image may be output if a confidence that the face
in the third unlabeled image is the same as the face in the seed
image.
[0189] Yet another embodiment of the present invention enables a
user to improve their search results by providing feedback to the
document discovery system. For example, when performing a search on
an internet search engine, patent or patent application search
product, etc., users may get a multitude of results in response to
their search query. An embodiment of the present invention enables
the user to review the suggested results from the search engine and
inform the engine of the relevance of one or more of the retrieved
results, e.g. "close, but not exactly what I wanted," "definitely
not," etc. As the user provides feedback to the engine, better
results are prioritized for the user to review.
[0190] In one embodiment, a method for document searching is
presented in FIG. 25. In step 2500, a search query is received. The
search query may be any type of query, including case-sensitive
queries, Boolean queries, approximate match queries, structured
queries, etc. In step 2502, documents based on the search query are
retrieved. Additionally, in step 2504 the documents are output, and
in step 2506 user-entered labels for at least some of the documents
are received, the labels being indicative of a relevance of the
document to the search query. For example, the user may indicate
whether a particular result returned from the query is relevant or
not. Also, in step 2508 a classifier is trained based on the search
query and the user-entered labels, and in step 2510 a document
classification technique is performed on the documents using the
classifier for reclassifying the documents. Further, in step 2512
identifiers of at least some of the documents are output based on
the classification thereof. The identifiers may be electronic
copies of the document themselves, portions thereof, titles
thereof, names thereof, file names thereof, pointers to the
documents, etc. The reclassified documents may also be output, with
those documents having a highest confidence being output first.
[0191] The document classification technique may include any type
of process, e.g. a transductive process, a support vector machine
process, a maximum entropy discrimination process, etc. Any
inductive or transductive technique described above may be used. In
a preferred approach, the classifier may be a transductive
classifier, and the transductive classifier may be trained through
iterative calculation using at least one predetermined cost factor,
the search query, and the documents, wherein for each iteration of
the calculations the cost factor may be adjusted as a function of
an expected label value, and the trained classifier may be used to
classify the documents. In addition, a data point label prior
probability for the search query and documents may be received,
wherein for each iteration of the calculations the data point label
prior probability may be adjusted according to an estimate of a
data point class membership probability.
[0192] A further embodiment of the present invention may be used
for improving ICR/OCR, and speech recognition. For example, many
embodiments of speech recognition programs and systems require the
operator to repeat a number of words to train the system. The
present invention can initially monitor the voice of a user for a
preset period of time to gather "unclassified" content, e.g., by
listening in to phone conversations. As a result, when the user
starts training the recognition system, the system utilizes
transductive learning to utilize the monitored speech to assist in
building a memory model.
[0193] In yet another embodiment, a method for verifying an
association of an invoice with an entity is presented in FIG. 26.
In step 2600, a classifier is trained based on an invoice format
associated with a first entity. The invoice format may refer to
either or both of the physical layout of markings on the invoice,
or characteristics such as keywords, invoice number, client name,
etc. on the invoice. In addition, in step 2602 a plurality of
invoices labeled as being associated with at least one of the first
entity and other entities are accessed, and in step 2604 a document
classification technique is performed on the invoices using the
classifier. For example, any inductive or transductive technique
described above may be used as a document classification technique.
For example, the document classification technique may include a
transductive process, support vector machine process, a maximum
entropy discrimination process, etc. Also, in step 2606 an
identifier of at least one of the invoices having a high
probability of not being associated with the first entity is
output.
[0194] Further, the classifier may be any type of classifier, for
example, a transductive classifier, and the transductive classifier
may be trained through iterative calculation using at least one
predetermined cost factor, at least one seed document, and the
invoices, wherein for each iteration of the calculations the cost
factor is adjusted as a function of an expected label value, and
using the trained classifier to classify the invoices. Also, a data
point label prior probability for the seed document and invoices
may be received, wherein for each iteration of the calculations the
data point label prior probability is adjusted according to an
estimate of a data point class membership probability.
[0195] One of the benefits afforded by the embodiments depicted
herein is the stability of the transductive algorithm. This
stability is achieved by scaling the cost factors and adjusting the
label prior probability. For example, in one embodiment a
transductive classifier is trained through iterative classification
using at least one cost factor, the labeled data points, and the
unlabeled data points as training examples. For each iteration of
the calculations, the unlabeled date point cost factor is adjusted
as a function of an expected label value. Additionally, for each
iteration of the calculations the data point label prior
probability is adjusted according to an estimate of a data point
class membership probability.
[0196] The workstation may have resident thereon an operating
system such as the Microsoft Windows.RTM. Operating System (OS), a
MAC OS, or UNIX operating system. It will be appreciated that a
preferred embodiment may also be implemented on platforms and
operating systems other than those mentioned. A preferred
embodiment may be written using JAVA, XML, C, and/or C++ language,
or other programming languages, along with an object oriented
programming methodology. Object oriented programming (OOP), which
has become increasingly used to develop complex applications, may
be used.
[0197] The above application uses transductive learning to overcome
the problem of very sparse data sets which plague inductive
face-recognition systems. This aspect of transductive learning is
not limited to this application and may be used to solve other
machine-learning problems that arise from sparse data.
[0198] Those skilled in the art could devise variations that are
within the scope and spirit of the various embodiments of the
invention disclosed herein. Further, the various features of the
embodiments disclosed herein can be used alone, or in varying
combinations with each other and are not intended to be limited to
the specific combination described herein. Thus, the scope of the
claims is not to be limited by the illustrated embodiments.
* * * * *