U.S. patent application number 10/224707 was filed with the patent office on 2003-01-30 for enhancing knowledge discovery using support vector machines in a distributed network environment.
Invention is credited to Barnhill, Stephen D..
Application Number | 20030023571 10/224707 |
Document ID | / |
Family ID | 22181776 |
Filed Date | 2003-01-30 |
United States Patent
Application |
20030023571 |
Kind Code |
A1 |
Barnhill, Stephen D. |
January 30, 2003 |
Enhancing knowledge discovery using support vector machines in a
distributed network environment
Abstract
A system and method for enhancing knowledge discovery from data
using a learning machine in general and a support vector machine in
particular in a distributed network environment. A customer may
transmit training data, test data and live data to a vendor's
server from a remote source, via a distributed network. The
customer may also transmit to the server identification information
such as a user name, a password and a financial account identifier.
The training data, test data and live data may be stored in a
storage device. Training data may then be pre-processed in order to
add meaning thereto. Pre-processing data may involve transforming
the data points and/or expanding the data points. By adding meaning
to the data, the learning machine is provided with a greater amount
of information for processing. With regard to support vector
machines in particular, the greater the amount of information that
is processed, the better generalizations about the data that may be
derived. The learning machine is therefore trained with the
pre-processed training data and is tested with test data that is
pre-processed in the same manner. The test output from the learning
machine is post-processed in order to determine if the knowledge
discovered from the test data is desirable. Post-processing
involves interpreting the test output into a format that may be
compared with the test data. Live data is pre-processed and input
into the trained and tested learning machine. The live output from
the learning machine may then be post-processed into a
computationally derived alphanumerical classifier for
interpretation by a human or computer automated process. Prior to
transmitting the alpha numerical classifier to the customer via the
distributed network, the server is operable to communicate with a
financial institution for the purpose of receiving funds from a
financial account of the customer identified by the financial
account identifier.
Inventors: |
Barnhill, Stephen D.;
(Savannah, GA) |
Correspondence
Address: |
JOHN S. PRATT, ESQ
KILPATRICK STOCKTON, LLP
1100 PEACHTREE STREET
SUITE 2800
ATLANTA
GA
30309
US
|
Family ID: |
22181776 |
Appl. No.: |
10/224707 |
Filed: |
August 21, 2002 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10224707 |
Aug 21, 2002 |
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09715832 |
Nov 17, 2000 |
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09715832 |
Nov 17, 2000 |
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09305345 |
May 1, 1999 |
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6157921 |
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60083961 |
May 1, 1998 |
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Current U.S.
Class: |
706/16 |
Current CPC
Class: |
G06K 9/6256 20130101;
G06K 9/6269 20130101; G06N 20/00 20190101; G06N 20/10 20190101 |
Class at
Publication: |
706/16 |
International
Class: |
G06E 001/00; G06E
003/00; G06G 007/00; G06F 015/18 |
Foreign Application Data
Date |
Code |
Application Number |
Mar 7, 1997 |
JP |
9-052608 |
Claims
What is claimed is:
1. A system for enhancing knowledge discovery using a support
vector machine comprising: a server in communication with a
distributed network for receiving a training data set, a test data
set, a live data set and a financial account identifier from a
remote source, the remote source also in communication with the
distributed network; one or more storage devices in communication
with the server for storing the training data set and the test data
set; a processor for executing a support vector machine; the
processor further operable for: collecting the training data set
from the one or more storage devices, pre-processing the training
data set to add meaning to each of a plurality of training data
points, inputting the pre-processed training data set into the
support vector machine so as to train the support vector machine,
in response to training of the support vector machine, collecting
the test data set from the database, pre-processing the test data
set in the same manner as was the training data set, inputting the
test data set into the trained support vector machine in order to
test the support vector machine, in response to receiving a test
output from the trained support vector machine, collecting the live
data set from the one or more storage devices, inputting the live
data set into the tested and trained support vector machine in
order to process the live data, in response to receiving a live
output from the support vector machine, post-processing the live
output to derive a computationally based alpha numerical
classifier, and transmitting the alphanumerical classifier to the
server; wherein the server is further operable for: communicating
with a financial institution in order to receive funds from a
financial account identified by the financial account identifier,
and in response to receiving the funds, transmitting the
alphanumerical identifier to the remote source or another remote
source.
2. The system of claim 1, wherein each training data point
comprises a vector having one or more coordinates; and wherein
pre-processing the-training data set to add meaning to each
training data point comprises: determining that the training data
point is dirty; and in response to determining that the training
data point is dirty, cleaning the training data point.
3. The system of claim 2, wherein cleaning the training data point
comprises deleting, repairing or replacing the data point.
4. The system of claim 1, wherein each training data point
comprises a vector having one or more original coordinates; and
wherein pre-processing the training data set to add meaning to each
training data point comprises adding dimensionality to each
training data point by adding one or more new coordinates to the
vector.
5. The system of claim 4, wherein the one or more new coordinates
added to the vector are derived by applying a transformation to one
or more of the original coordinates.
6. The system of claim 4, wherein the transformation is based on
expert knowledge.
7. The system of claim 4, wherein the transformation is
computationally derived.
8. The system of claim 4, wherein the training data set comprises a
continuous variable; and wherein the transformation comprises
optimally categorizing the continuous variable of the training data
set.
9. The system of claim 1, wherein the knowledge to be discovered
from the data relates to a regression or density estimation;
wherein the support vector machine produces a training output
comprising a continuous variable; and wherein the processor is
further operable for post-processing the training output by
optimally categorizing the training output to derive cutoff points
in the continuous variable.
10. The system of claim 1, wherein the processor is further
operable for: in response to comparing each of the test outputs
with each other, determining that none of the test outputs is the
optimal solution; adjusting the different kernels of one or more of
the plurality of support vector machines; and in response to
adjusting the selection of the different kernels, retraining and
retesting each of the plurality of support vector machines.
Description
RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Patent Application Serial No. 60/083,961, filed May 1, 1998.
TECHNICAL FIELD
[0002] The present invention relates to the use of learning
machines to discover knowledge from data. More particularly, the
present invention relates to optimizations for learning machines
and associated input and output data, in order to enhance the
knowledge discovered from data.
BACKGROUND OF THE INVENTION
[0003] Knowledge discovery is the most desirable end product of
data collection. Recent advancements in database technology have
lead to an explosive growth in systems and methods for generating,
collecting and storing vast amounts of data. While database
technology enables efficient collection and storage of large data
sets, the challenge of facilitating human comprehension of the
information in this data is growing ever more difficult. With many
existing techniques the problem has become unapproachable. Thus,
there remains a need for a new generation of automated knowledge
discovery tools.
[0004] As a specific example, the Human Genome Project is
populating a multi-gigabyte database describing the human genetic
code. Before this mapping of the human genome is complete (expected
in 2003), the size of the database is expected to grow
significantly. The vast amount of data in such a database
overwhelms traditional tools for data analysis, such as
spreadsheets and ad hoc queries. Traditional methods of data
analysis may be used to create informative reports from data, but
do not have the ability to intelligently and automatically assist
humans in analyzing and finding patterns of useful knowledge in
vast amounts of data. Likewise, using traditionally accepted
reference ranges and standards for interpretation, it is often
impossible for humans to identify patterns of useful knowledge even
with very small amounts of data.
[0005] One recent development that has been shown to be effective
in some examples of machine learning is the back-propagation neural
network. Back-propagation neural networks are learning machines
that may be trained to discover knowledge in a data set that is not
readily apparent to a human. However, there are various problems
with back-propagation neural network approaches that prevent neural
networks from being well-controlled learning machines. For example,
a significant drawback of back-propagation neural networks is that
the empirical risk function may have many local minimums, a case
that can easily obscure the optimal solution from discovery by this
technique. Standard optimization procedures employed by
back-propagation neural networks may convergence to a minimum, but
the neural network method cannot guarantee that even a localized
minimum is attained much less the desired global minimum. The
quality of the solution obtained from a neural network depends on
many factors. In particular the skill of the practitioner
implementing the neural network determines the ultimate benefit,
but even factors as seemingly benign as the random selection of
initial weights can lead to poor results. Furthermore, the
convergence of the gradient based method used in neural network
learning is inherently slow. A further drawback is that the sigmoid
function has a scaling factor, which affects the quality of
approximation. Possibly the largest limiting factor of neural
networks as related to knowledge discovery is the "curse of
dimensionality" associated with the disproportionate growth in
required computational time and power for each additional feature
or dimension in the training data.
[0006] The shortcomings of neural networks are overcome using
support vector machines. In general terms, a support vector machine
maps input vectors into high dimensional feature space through
non-linear mapping function, chosen a priori. In this high
dimensional feature space, an optimal separating hyperplane is
constructed. The optimal hyperplane is then used to determine
things such as class separations, regression fit, or accuracy in
density estimation.
[0007] Within a support vector machine, the dimensionally of the
feature space may be huge. For example, a fourth degree polynomial
mapping function causes a 200 dimensional input space to be mapped
into a 1.6 billionth dimensional feature space. The kernel trick
and the Vapnik-Chervonenkis dimension allow the support vector
machine to thwart the "curse of dimensionality" limiting other
methods and effectively derive generalizable answers from this very
high dimensional feature space.
[0008] If the training vectors are separated by the optimal
hyperplane (or generalized optimal hyperplane), then the
expectation value of the probability of committing an error on a
test example is bounded by the examples in the training set. This
bound depends neither on the dimensionality of the feature space,
nor on the norm of the vector of coefficients, nor on the bound of
the number of the input vectors. Therefore, if the optimal
hyperplane can be constructed from a small number of support
vectors relative to the training set size, the generalization
ability will be high, even in infinite dimensional space.
[0009] As such, support vector machines provide a desirable
solution for the problem of discovering knowledge from vast amounts
of input data. However, the ability of a support vector machine to
discover knowledge from a data set is limited in proportion to the
information included within the training data set. Accordingly,
there exists a need for a system and method for pre-processing data
so as to augment the training data to maximize the knowledge
discovery by the support vector machine.
[0010] Furthermore, the raw output from a support vector machine
may not fully disclose the knowledge in the most readily
interpretable form. Thus, there further remains a need for a system
and method for post-processing data output from a support vector
machine in order to maximize the value of the information delivered
for human or further automated processing.
[0011] In addition, a the ability of a support vector machine to
discover knowledge from data is limited by the selection of a
kernel. Accordingly, there remains a need for an improved system
and method for selecting and/or creating a desired kernel for a
support vector machine.
SUMMARY OF THE INVENTION
[0012] The present invention meets the above described needs by
providing a system and method for enhancing knowledge discovered
from data using a learning machine in general and a support vector
machine in particular. A training data set is pre-processed in
order to allow the most advantageous application of the learning
machine. Each training data point comprises a vector having one or
more coordinates. Pre-processing the training data set may comprise
identifying missing or erroneous data points and taking appropriate
steps to correct the flawed data or as appropriate remove the
observation or the entire field from the scope of the problem.
Pre-processing the training data set may also comprise adding
dimensionality to each training data point by adding one or more
new coordinates to the vector. The new coordinates added to the
vector may be derived by applying a transformation to one or more
of the original coordinates. The transformation may be based on
expert knowledge, or may be computationally derived. In a situation
where the training data set comprises a continuous variable, the
transformation may comprise optimally categorizing the continuous
variable of the training data set.
[0013] The support vector machine is trained using the
pre-processed training data set. In this manner, the additional
representations of the training data provided by the preprocessing
may enhance the learning machine's ability to discover knowledge
therefrom. In the particular context of support vector machines,
the greater the dimensionality of the training set, the higher the
quality of the generalizations that may be derived therefrom. When
the knowledge to be discovered from the data relates to a
regression or density estimation or where the training output
comprises a continuous variable, the training output may be
post-processed by optimally categorizing the training output to
derive categorizations from the continuous variable.
[0014] A test data set is pre-processed in the same manner as was
the training data set. Then, the trained learning machine is tested
using the pre-processed test data set. A test output of the trained
learning machine may be post-processing to determine if the test
output is an optimal solution. Post-processing the test output may
comprise interpreting the test output into a format that may be
compared with the test data set. Alternative post-processing steps
may enhance the human interpretability or suitability for
additional processing of the output data.
[0015] In the context of a support vector machine, the present
invention also provides for the selection of a kernel prior to
training the support vector machine. The selection of a kernel may
be based on prior knowledge of the specific problem being addressed
or analysis of the properties of any available data to be used with
the learning machine and is typically dependant on the nature of
the knowledge to be discovered from the data. Optionally, an
iterative process comparing postprocessed training outputs or test
outputs can be applied to make a determination as to which
configuration provides the optimal solution. If the test output is
not the optimal solution, the selection of the kernel may be
adjusted and the support vector machine may be retrained and
retested. When it is determined that the optimal solution has been
identified, a live data set may be collected and pre-processed in
the same manner as was the training data set. The preprocessed live
data set is input into the learning machine for processing. The
live output of the learning machine may then be post-processed by
interpreting the live output into a computationally derived
alphanumeric classifier.
[0016] In an exemplary embodiment a system is provided enhancing
knowledge discovered from data using a support vector machine. The
exemplary system comprises a storage device for storing a training
data set and a test data set, and a processor for executing a
support vector machine. The processor is also operable for
collecting the training data set from the database, pre-processing
the training data set to enhance each of a plurality of training
data points, training the support vector machine using the
pre-processed training data set, collecting the test data set from
the database, pre-processing the test data set in the same manner
as was the training data set, testing the trained support vector
machine using the pre-processed test data set, and in response to
receiving the test output of the trained support vector machine,
post-processing the test output to determine if the test output is
an optimal solution. The exemplary system may also comprise a
communications device for receiving the test data set and the
training data set from a remote source. In such a case, the
processor may be operable to store the training data set in the
storage device prior pre-processing of the training data set and to
store the test data set in the storage device prior pre-processing
of the test data set. The exemplary system may also comprise a
display device for displaying the post-processed test data. The
processor of the exemplary system may further be operable for
performing each additional function described above. The
communications device may be further operable to send a
computationally derived alphanumeric classifier to a remote
source.
[0017] In an exemplary embodiment, a system and method are provided
for enhancing knowledge discovery from data using multiple learning
machines in general and multiple support vector machines in
particular. Training data for a learning machine is pre-processed
in order to add meaning thereto. Pre-processing data may involve
transforming the data points and/or expanding the data points. By
adding meaning to the data, the learning machine is provided with a
greater amount of information for processing. With regard to
support vector machines in particular, the greater the amount of
information that is processed, the better generalizations about the
data that may be derived. Multiple support vector machines, each
comprising distinct kernels, are trained with the pre-processed
training data and are tested with test data that is pre-processed
in the same manner. The test outputs from multiple support vector
machines are compared in order to determine which of the test
outputs if any represents a optimal solution. Selection of one or
more kernels may be adjusted and one or more support vector
machines may be retrained and retested. When it is determined that
an optimal solution has been achieved, live data is pre-processed
and input into the support vector machine comprising the kernel
that produced the optimal solution. The live output from the
learning machine may then be post-processed into a computationally
derived alphanumerical classifier for interpretation by a human or
computer automated process.
[0018] In another exemplary embodiment, a system and method are
provided for optimally categorizing a continuous variable. A data
set representing a continuous variable comprises data points that
each comprise a sample from the continuous variable and a class
identifier. A number of distinct class identifiers within the data
set is determined and a number of candidate bins is determined
based on the range of the samples and a level of precision of the
samples within the data set. Each candidate bin represents a
sub-range of the samples. For each candidate bin, the entropy of
the data points falling within the candidate bin is calculated.
Then, for each sequence of candidate bins that have a minimized
collective entropy, a cutoff point in the range of samples is
defined to be at the boundary of the last candidate bin in the
sequence of candidate bins. As an iterative process, the collective
entropy for different combinations of sequential candidate bins may
be calculated. Also the number of defined cutoff points may be
adjusted in order to determine the optimal number of cutoff point,
which is based on a calculation of minimal entropy. As mentioned,
the exemplary system and method for optimally categorizing a
continuous variable may be used for pre-processing data to be input
into a learning machine and for post-processing output of a
learning machine.
[0019] In still another exemplary embodiment, a system and method
are provided for for enhancing knowledge discovery from data using
a learning machine in general and a support vector machine in
particular in a distributed network environment. A customer may
transmit training data, test data and live data to a vendor's
server from a remote source, via a distributed network. The
customer may also transmit to the server identification information
such as a user name, a password and a financial account identifier.
The training data, test data and live data may be stored in a
storage device. Training data may then be pre-processed in order to
add meaning thereto. Pre-processing data may involve transforming
the data points and/or expanding the data points. By adding meaning
to the data, the learning machine is provided with a greater amount
of information for processing. With regard to support vector
machines in particular, the greater the amount of information that
is processed, the better generalizations about the data that may be
derived. The learning machine is therefore trained with the
pre-processed training data and is tested with test data that is
pre-processed in the same manner. The test output from the learning
machine is post-processed in order to determine if the knowledge
discovered from the test data is desirable. Post-processing
involves interpreting the test output into a format that may be
compared with the test data. Live data is pre-processed and input
into the trained and tested learning machine. The live output from
the learning machine may then be post-processed into a
computationally derived alphanumerical classifier for
interpretation by a human or computer automated process. Prior to
transmitting the alpha numerical classifier to the customer via the
distributed network, the server is operable to communicate with a
financial institution for the purpose of receiving funds from a
financial account of the customer identified by the financial
account identifier.
BRIEF DESCRIPTION OF THE DRAWINGS
[0020] FIG. 1 is a flowchart illustrating an exemplary general
method for increasing knowledge that may be discovered from data
using a learning machine.
[0021] FIG. 2 is a flowchart illustrating an exemplary method for
increasing knowledge that may be discovered from data using a
support vector machine.
[0022] FIG. 3 is a flowchart illustrating an exemplary optimal
categorization method that may be used in a stand-alone
configuration or in conjunction with a learning machine for
pre-processing or post-processing techniques in accordance with an
exemplary embodiment of the present invention.
[0023] FIG. 4 illustrates an exemplary unexpanded data set that may
be input into a support vector machine.
[0024] FIG. 5 illustrates an exemplary post-processed output
generated by a support vector machine using the data set of FIG.
4.
[0025] FIG. 6 illustrates an exemplary expanded data set that may
be input into a support vector machine.
[0026] FIG. 7 illustrates an exemplary post-processed output
generated by a support vector machine using the data set of FIG.
6.
[0027] FIG. 8 illustrates exemplary input and output for a
standalone application of the optimal categorization method of FIG.
3.
[0028] FIG. 9 is a comparison of exemplary post-processed output
from a first support vector machine comprising a linear kernel and
a second support vector machine comprising a polynomial kernel.
[0029] FIG. 10 is a functional block diagram illustrating an
exemplary operating environment for an exemplary embodiment of the
present invention.
[0030] FIG. 11 is a functional block diagram illustrating an
alternate exemplary operating environment for an alternate
embodiment of the present invention.
[0031] FIG. 12 is a functional block diagram illustrating an
exemplary network operating environment for implementation of a
further alternate embodiment of the present invention.
DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS
[0032] The present invention provides improved methods for
discovering knowledge from data using learning machines. While
several examples of learning machines exist and advancements are
expected in this field, the exemplary embodiments of the present
invention focus on the support vector machine. As is known in the
art, learning machines comprise algorithms that may be trained to
generalize using data with known outcomes. Trained learning machine
algorithms may then applied to cases of unknown outcome for
prediction. For example, a learning machine may be trained to
recognize patterns in data, estimate regression in data or estimate
probability density within data. Learning machines may be trained
to solve a wide variety of problems as known to those of ordinary
skill in the art. A trained learning machine may optionally be
tested using test data to ensure that its output is validated
within an acceptable margin of error. Once a learning machine is
trained and tested, live data may be input therein. The live output
of a learning machine comprises knowledge discovered from all of
the training data as applied to the live data.
[0033] A first aspect of the present invention seeks to enhance
knowledge discovery by optionally pre-processing data prior to
using the data to train a learning machine and/or optionally
post-processing the output from a learning machine. Generally
stated, pre-processing data comprises reformatting or augmenting
the data in order to allow the learning machine to be applied most
advantageously. Similarly, post-processing involves interpreting
the output of a learning machine in order to discover meaningful
characteristics thereof. The meaningful characteristics to be
ascertained from the output may be problem or data specific.
Post-processing involves interpreting the output into a form that
comprehendible by a human or one that is comprehendible by a
computer.
[0034] Exemplary embodiments of the present invention will
hereinafter be described with reference to the drawing, in which
like numerals indicate like elements throughout the several
figures. FIG. 1 is a flowchart illustrating a general method 100
for enhancing knowledge discovery using learning machines. The
method 100 begins at starting block 101 and progresses to step 102
where a specific problem is formalized for application of knowledge
discovery through machine learning. Particularly important is a
proper formulation of the desired output of the learning machine.
For instance, in predicting future performance of an individual
equity instrument, or a market index, a learning machine is likely
to achieve better performance when predicting the expected future
change rather than predicting the future price level. The future
price expectation can later be derived in a post-processing step as
will be discussed later in this specification.
[0035] After problem formalization, step 103 addresses training
data collection. Training data comprises a set of data points
having known characteristics. Training data may be collected from
one or more local and/or remote sources. The collection of training
data may be accomplished manually or by way of an automated
process, such as known electronic data transfer methods.
Accordingly, an exemplary embodiment of the present invention may
be implemented in a networked computer environment. Exemplary
operating environments for implementing various embodiments of the
present invention will be described in detail with respect to FIGS.
10-12.
[0036] Next, at step 104 the collected training data is optionally
pre-processed in order to allow the learning machine to be applied
most advantageously toward extraction of the knowledge inherent to
the training data. During this pre-processing stage the training
data can optionally be expanded through transformations,
combinations or manipulation of individual or multiple measures
within the records of the training data. As used herein, expanding
data is meant to refer to altering the dimensionality of the input
data by changing the number of observations available to determine
each input point (alternatively, this could be described as adding
or deleting columns within a database table). By way of
illustration, a data point may comprise the coordinates (1,4,9). An
expanded version of this data point may result in the coordinates
(1,1,4,2,9,3). In this example, it may be seen that the coordinates
added to the expanded data point are based on a square-root
transformation of the original coordinates. By adding
dimensionality to the data point, this expanded data point provides
a varied representation of the input data that is potentially more
meaningful for knowledge discovery by a learning machine. Data
expansion in this sense affords opportunities for learning machines
to discover knowledge not readily apparent in the unexpanded
training data.
[0037] Expanding data may comprise applying any type of meaningful
transformation to the data and adding those transformations to the
original data. The criteria for determining whether a
transformation is meaningful may depend on the input data itself
and/or the type of knowledge that is sought from the data.
Illustrative types of data transformations include: addition of
expert information; labeling; binary conversion; sine, cosine,
tangent, cotangent, and other trigonometric transformation;
clustering; scaling; probabilistic and statistical analysis;
significance testing; strength testing; searching for 2-D
regularities; Hidden Markov Modeling; identification of equivalence
relations; application of contingency tables; application of graph
theory principles; creation of vector maps; addition, subtraction,
multiplication, division, application of polynomial equations and
other algebraic transformations; identification of proportionality;
determination of discriminatory power; etc. In the context of
medical data, potentially meaningful transformations include:
association with known standard medical reference ranges;
physiologic truncation; physiologic combinations; biochemical
combinations; application of heuristic rules; diagnostic criteria
determinations; clinical weighting systems; diagnostic
transformations; clinical transformations; application of expert
knowledge; labeling techniques; application of other domain
knowledge; Bayesian network knowledge; etc. These and other
transformations, as well as combinations thereof, will occur to
those of ordinary skill in the art.
[0038] Those skilled in the art should also recognize that data
transformations may be performed without adding dimensionality to
the data points. For example a data point may comprise the
coordinate (A, B, C). A transformed version of this data point may
result in the coordinates (1, 2, 3), where the coordinate "1" has
some known relationship with the coordinate "A," the coordinate "2"
has some known relationship with the coordinate "B," and the
coordinate "3" has some known relationship with the coordinate "C."
A transformation from letters to numbers may be required, for
example, if letters are not understood by a learning machine. Other
types of transformations are possible without adding dimensionality
to the data points, even with respect to data that is originally in
numeric form. Furthermore, it should be appreciated that
pre-processing data to add meaning thereto may involve analyzing
incomplete, corrupted or otherwise "dirty" data. A learning machine
cannot process "dirty" data in a meaningful manner. Thus, a
pre-processing step may involve cleaning up a data set in order to
remove, repair or replace dirty data points.
[0039] Returning to FIG. 1, the exemplary method 100 continues at
step 106, where the learning machine is trained using the
pre-processed data. As is known in the art, a learning machine is
trained by adjusting its operating parameters until a desirable
training output is achieved. The determination of whether a
training output is desirable may be accomplished either manually or
automatically by comparing the training output to the known
characteristics of the training data. A learning machine is
considered to be trained when its training output is within a
predetermined error threshold from the known characteristics of the
training data. In certain situations, it may be desirable, if not
necessary, to post-process the training output of the learning
machine at step 107. As mentioned, post-processing the output of a
learning machine involves interpreting the output into a meaningful
form. In the context of a regression problem, for example, it may
be necessary to determine range categorizations for the output of a
learning machine in order to determine if the input data points
were correctly categorized. In the example of a pattern recognition
problem, it is often not necessary to post-process the training
output of a learning machine.
[0040] At step 108, test data is optionally collected in
preparation for testing the trained learning machine. Test data may
be collected from one or more local and/or remote sources. In
practice, test data and training data may be collected from the
same source(s) at the same time. Thus, test data and training data
sets can be divided out of a common data set and stored in a local
storage medium for use as different input data sets for a learning
machine. Regardless of how the test data is collected, any test
data used must be pre-processed at step 110 in the same manner as
was the training data. As should be apparent to those skilled in
the art, a proper test of the learning may only be accomplished by
using testing data of the same format as the training data. Then,
at step 112 the learning machine is tested using the pre-processed
test data, if any. The test output of the learning machine is
optionally post-processed at step 114 in order to determine if the
results are desirable. Again, the post processing step involves
interpreting the test output into a meaningful form. The meaningful
form may be one that is comprehendible by a human or one that is
comprehendible by a computer. Regardless, the test output must be
post-processed into a form which may be compared to the test data
to determine whether the results were desirable. Examples of
post-processing steps include but are not limited of the following:
optimal categorization determinations, scaling techniques (linear
and non-linear), transformations (linear and non-linear), and
probability estimations. The method 100 ends at step 116.
[0041] FIG. 2 is a flow chart illustrating an exemplary method 200
for enhancing knowledge that may be discovered from data using a
specific type of learning machine known as a support vector machine
(SVM). A SVM implements a specialized algorithm for providing
generalization when estimating a multi-dimensional function from a
limited collection of data. A SVM may be particularly useful in
solving dependency estimation problems. More specifically, a SVM
may be used accurately in estimating indicator functions (e.g.
pattern recognition problems) and real-valued functions (e.g.
function approximation problems, regression estimation problems,
density estimation problems, and solving inverse problems). The SMV
was originally developed by Vladimir N. Vapnik. The concepts
underlying the SVM are explained in detail in his book, entitled
Statistical Leaning Theory (John Wiley & Sons, Inc. 1998),
which is herein incorporated by reference in its entirety.
Accordingly, a familiarity with SVMs and the terminology used
therewith are presumed throughout this specification.
[0042] The exemplary method 200 begins at starting block 201 and
advances to step 202, where a problem is formulated and then to
step 203, where a training data set is collected. As was described
with reference to FIG. 1, training data may be collected from one
or more local and/or remote sources, through a manual or automated
process. At step 204 the training data is optionally pre-processed.
Again, pre-processing data comprises enhancing meaning within the
training data by cleaning the data, transforming the data and/or
expanding the data. Those skilled in the art should appreciate that
SVMs are capable of processing input data having extremely large
dimensionality. In fact, the larger the dimensionality of the input
data, the better generalizations a SVM is able to calculate.
Therefore, while training data transformations are possible that do
not expand the training data, in the specific context of SVMs it is
preferable that training data be expanded by adding meaningful
information thereto.
[0043] At step 206 a kernel is selected for the SVM. As is known in
the art, different kernels will cause a SVM to produce varying
degrees of quality in the output for a given set of input data.
Therefore, the selection of an appropriate kernel may be essential
to the desired quality of the output of the SVM. In one embodiment
of the present invention, a kernel may be chosen based on prior
performance knowledge. As is known in the art, exemplary kernels
include polynomial kernels, radial basis classifier kernels, linear
kernels, etc. In an alternate embodiment, a customized kernel may
be created that is specific to a particular problem or type of data
set. In yet another embodiment, the multiple SVMs may be trained
and tested simultaneously, each using a different kernel. The
quality of the outputs for each simultaneously trained and tested
SVM may be compared using a variety of selectable or weighted
metrics (see step 222) to determine the most desirable kernel.
[0044] Next, at step 208 the pre-processed training data is input
into the SVM. At step 210, the SVM is trained using the
pre-processed training data to generate an optimal hyperplane.
Optionally, the training output of the SVM may then be
post-processed at step 211. Again, post-processing of training
output may be desirable, or even necessary, at this point in order
to properly calculate ranges or categories for the output. At step
212 test data is collected similarly to previous descriptions of
data collection. The test data is pre-processed at step 214 in the
same manner as was the training data above. Then, at step 216 the
pre-processed test data is input into the SVM for processing in
order to determine whether the SVM was trained in a desirable
manner. The test output is received from the SVM at step 218 and is
optionally post-processed at step 220.
[0045] Based on the post-processed test output, it is determined at
step 222 whether an optimal minimum was achieved by the SVM. Those
skilled in the art should appreciate that a SVM is operable to
ascertain an output having a global minimum error. However, as
mentioned above output results of a SVM for a given data set will
typically vary in relation to the selection of a kernel. Therefore,
there are in fact multiple global minimums that may be ascertained
by a SVM for a given set of data. As used herein, the term "optimal
minimum" or "optimal solution" refers to a selected global minimum
that is considered to be optimal (e.g. the optimal solution for a
given set of problem specific, pre-established criteria) when
compared to other global minimums ascertained by a SVM.
Accordingly, at step 222 determining whether the optimal minimum
has been ascertained may involve comparing the output of a SVM with
a historical or predetermined value. Such a predetermined value may
be dependant on the test data set. For example, in the context of a
pattern recognition problem where a data point are classified by a
SVM as either having a certain characteristic or not having the
characteristic, a global minimum error of 50% would not be optimal.
In this example, a global minimum of 50% is no better than the
result that would be achieved by flipping a coin to determine
whether the data point had the certain characteristic. As another
example, in the case where multiple SVMs are trained and tested
simultaneously with varying kernels, the outputs for each SVM may
be compared with each other SVM's outputs to determine the
practical optimal solution for that particular set of kernels. The
determination of whether an optimal solution has been ascertained
may be performed manually or through an automated comparison
process.
[0046] If it is determined that the optimal minimum has not been
achieved by the trained SVM, the method advances to step 224, where
the kernel selection is adjusted. Adjustment of the kernel
selection may comprise selecting one or more new kernels or
adjusting kernel parameters. Furthermore, in the case where
multiple SVMs were trained and tested simultaneously, selected
kernels may be replaced or modified while other kernels may be
re-used for control purposes. After the kernel selection is
adjusted, the method 200 is repeated from step 208, where the
pre-processed training data is input into the SVM for training
purposes. When it is determined at step 222 that the optimal
minimum has been achieved, the method advances to step 226, where
live data is collected similarly as described above. The desired
output characteristics that were known with respect to the training
data and the test data are not known with respect to the live
data.
[0047] At step 228 the live data is pre-processed in the same
manner as was the training data and the test data. At step 230, the
live pre-processed data is input into the SVM for processing. The
live output of the SVM is received at step 232 and is
post-processed at step 234. In one embodiment of the present
invention, post-processing comprises converting the output of the
SVM into a computationally derived alpha-numerical classifier, for
interpretation by a human or computer. Preferably, the
alphanumerical classifier comprises a single value that is easily
comprehended by the human or computer. The method 200 ends at step
236.
[0048] FIG. 3 is a flow chart illustrating an exemplary optimal
categorization method 300 that may be used for pre-processing data
or post-processing output from a learning machine in accordance
with an exemplary embodiment of the present invention.
Additionally, as will be described below, the exemplary optimal
categorization method may be used as a stand-alone categorization
technique, independent from learning machines. The exemplary
optimal categorization method 300 begins at starting block 301 and
progresses to step 302, where an input data set is received. The
input data set comprises a sequence of data samples from a
continuous variable. The data samples fall within two or more
classification categories. Next, at step 304 the bin and
class-tracking variables are initialized. As is known in the art,
bin variables relate to resolution and class-tracking variables
relate to the number of classifications within the data set.
Determining the values for initialization of the bin and
class-tracking variables may be performed manually or through an
automated process, such as a computer program from analyzing the
input data set. At step 306, the data entropy for each bin is
calculated. Entropy is a mathematical quantity that measures the
uncertainty of a random distribution. In the exemplary method 300,
entropy is used to gauge the gradations of the input variable so
that maximum classification capability is achieved.
[0049] The method 300 produces a series of "cuts" on the continuous
variable, such that the continuous variable may be divided into
discrete categories. The cuts selected by the exemplary method 300
are optimal in the sense that the average entropy of each resulting
discrete category is minimized. At step 308, a determination is
made as to whether all cuts have been placed within input data set
comprising the continuous variable. If all cuts have not been
placed, sequential bin combinations are tested for cutoff
determination at step 310. From step 310, the exemplary method 300
loops back through step 306 and returns to step 308 where it is
again determined whether all cuts have been placed within input
data set comprising the continuous variable. When all cuts have
been placed, the entropy for the entire system is evaluated at step
309 and compared to previous results from testing more or fewer
cuts. If it cannot be concluded that a minimum entropy state has
been determined, then other possible cut selections must be
evaluated and the method proceeds to step 311. From step 311 a
heretofore untested selection for number of cuts is chosen and the
above process is repeated from step 304. When either the limits of
the resolution determined by the bin width has been tested or the
convergence to a minimum solution has been identified, the optimal
classification criteria is output at step 312 and the exemplary
optimal categorization method 300 ends at step 314.
[0050] The optimal categorization method 300 takes advantage of
dynamic programming techniques. As is known in the art, dynamic
programming techniques may be used to significantly improve the
efficiency of solving certain complex problems through carefully
structuring an algorithm to reduce redundant calculations. In the
optimal categorization problem, the straightforward approach of
exhaustively searching through all possible cuts in the continuous
variable data would result in an algorithm of exponential
complexity and would render the problem intractable for even
moderate sized inputs. By taking advantage of the additive property
of the target function, in this problem the average entropy, the
problem may be divide into a series of sub-problems. By properly
formulating algorithmic sub-structures for solving each sub-problem
and storing the solutions of the sub-problems, a great amount of
redundant computation may be identified and avoided. As a result of
using the dynamic programming approach, the exemplary optimal
categorization method 300 may be implemented as an algorithm having
a polynomial complexity, which may be used to solve large sized
problems.
[0051] As mentioned above, the exemplary optimal categorization
method 300 may be used in pre-processing data and/or
post-processing the output of a learning machine. For example, as a
pre-processing transformation step, the exemplary optimal
categorization method 300 may be used to extract classification
information from raw data. As a post-processing technique, the
exemplary optimal range categorization method may be used to
determine the optimal cut-off values for markers objectively based
on data, rather than relying on ad hoc approaches. As should be
apparent, the exemplary optimal categorization method 300 has
applications in pattern recognition, classification, regression
problems, etc. The exemplary optimal categorization method 300 may
also be used as a stand-alone categorization technique, independent
from SVMs and other learning machines. An exemplary stand-alone
application of the optimal categorization method 300 will be
described with reference to FIG. 8.
[0052] FIG. 4 illustrates an exemplary unexpanded data set 400 that
may be used as input for a support vector machine. This data set
400 is referred to as "unexpanded" because no additional
information has been added thereto. As shown, the unexpanded data
set comprises a training data set 402 and a test data set 404. Both
the unexpanded training data set 402 and the unexpanded test data
set 404 comprise data points, such as exemplary data point 406,
relating to historical clinical data from sampled medical patients.
The data set 400 may be used to train a SVM to determine whether a
breast cancer patient will experience a recurrence or not.
[0053] Each data point includes five input coordinates, or
dimensions, and an output classification shown as 406a-f which
represent medical data collected for each patient. In particular,
the first coordinate 406a represents "Age," the second coordinate
406b represents "Estrogen Receptor Level," the third coordinate
406c represents "Progesterone Receptor Level," the fourth
coordinate 406d represents "Total Lymph Nodes Extracted," the fifth
coordinate 406e represents "Positive (Cancerous) Lymph Nodes
Extracted," and the output classification 406f, represents the
"Recurrence Classification." The important known characteristic of
the data 400 is the output classification 406f (Recurrence
Classification), which, in this example, indicates whether the
sampled medical patient responded to treatment favorably without
recurrence of cancer ("-1") or responded to treatment negatively
with recurrence of cancer ("1"). This known characteristic will be
used for learning while processing the training data in the SVM,
will be used in an evaluative fashion after the test data is input
into the SVM thus creating a "blind" test, and will obviously be
unknown in the live data of current medical patients.
[0054] FIG. 5 illustrates an exemplary test output 502 from a SVM
trained with the unexpanded training data set 402 and tested with
the unexpanded data set 404 shown in FIG. 4. The test output 502
has been post-processed to be comprehensible by a human or
computer. As indicated, the test output 502 shows that 24 total
samples (data points) were examined by the SVM and that the SVM
incorrectly identified four of eight positive samples (50%) and
incorrectly identified 6 of sixteen negative samples (37.5%).
[0055] FIG. 6 illustrates an exemplary expanded data set 600 that
may be used as input for a support vector machine. This data set
600 is referred to as "expanded" because additional information has
been added thereto. Note that aside from the added information, the
expanded data set 600 is identical to the unexpanded data set 400
shown in FIG. 4. The additional information supplied to the
expanded data set has been supplied using the exemplary optimal
range categorization method 300 described with reference to FIG. 3.
As shown, the expanded data set comprises a training data set 602
and a test data set 604. Both the expanded training data set 602
and the expanded test data set 604 comprise data points, such as
exemplary data point 606, relating to historical data from sampled
medical patients. Again, the data set 600 may be used to train a
SVM to learn whether a breast cancer patient will experience a
recurrence of the disease.
[0056] Through application of the exemplary optimal categorization
method 300, each expanded data point includes twenty coordinates
(or dimensions) 606a1-3 through 606e1-3, and an output
classification 606f, which collectively represent medical data and
categorization transformations thereof for each patient. In
particular, the first coordinate 606a represents "Age," the second
coordinate through the fourth coordinate 606a1-606a3 are variables
that combine to represent a category of age. For example, a range
of ages may be categorized, for example, into "young" "middle-aged"
and "old" categories respective to the range of ages present in the
data. As shown, a string of variables "0" (606a1), "0" (606a2), "1"
(606a3) may be used to indicate that a certain age value is
categorized as "old." Similarly, a string of variables "0" (606a1),
"1" (606a2), "0" (606a3) may be used to indicate that a certain age
value is categorized as "middle-aged." Also, a string of variables
"1" (606a1), "0" (606a2), "0" (606a1) may be used to indicate that
a certain age value is categorized as "young." From an inspection
of FIG. 6, it may be seen that the optimal categorization of the
range of "Age" 606a values, using the exemplary method 300, was
determined to be 31-33="young," 34="middle-aged" and 35-49="old."
The other coordinates, namely coordinate 606b "Estrogen Receptors
Level," coordinate 606c "Progesterone Receptor Level," coordinate
606d "Total Lymph Nodes Extracted;" and coordinate 606e "Positive
(Cancerous) Lymph Nodes Extracted," have each been optimally
categorized in a similar manner.
[0057] FIG. 7 illustrates an exemplary expanded test output 702
from a SVM trained with the expanded training data set 602 and
tested with the expanded data set 604 shown in FIG. 6. The expanded
test output 702 has been post-processed to be comprehensible by a
human or computer. As indicated, the expanded test output 702 shows
that 24 total samples (data points) were examined by the SVM and
that the SVM incorrectly identified four of eight positive samples
(50%) and incorrectly identified four of sixteen negative samples
(25%). Accordingly, by comparing this expanded test output 702 with
the unexpanded test output 502 of FIG. 5, it may be seen that the
expansion of the data points leads to improved results (i.e. a
lower global minimum error), specifically a reduced instance of
patients who would unnecessarily be subjected to follow-up cancer
treatments.
[0058] FIG. 8 illustrates an exemplary input and output for a stand
alone application of the optimal categorization method 300
described in FIG. 3. In the example of FIG. 8, the input data set
801 comprises a "Number of Positive Lymph Nodes" 802 and a
corresponding "Recurrence Classification" 804. In this example, the
optimal categorization method 300 has been applied to the input
data set 801 in order to locate the optimal cutoff point for
determination of treatment for cancer recurrence, based solely upon
the number of positive lymph nodes collected in a post-surgical
tissue sample. The well-known clinical standard is to prescribe
treatment for any patient with at least three positive nodes.
However, the optimal categorization method 300 demonstrates that
the optimal cutoff 806, based upon the input data 801, should be at
the higher value of 5.5 lymph nodes, which corresponds to a
clinical rule prescribing follow-up treatments in patients with at
least six positive lymph nodes.
[0059] As shown in the comparison table 808, the prior art accepted
clinical cutoff point (.gtoreq.3.0) resulted in 47% correctly
classified recurrences and 71% correctly classified
non-recurrences. Accordingly, 53% of the recurrences were
incorrectly classified (further treatment was improperly not
recommended) and 29% of the non-recurrences were incorrectly
classified (further treatment was incorrectly recommended). By
contrast, the cutoff point determined by the optimal categorization
method 300 (24 5.5) resulted in 33% correctly classified
recurrences and 97% correctly classified non-recurrences.
Accordingly, 67% of the recurrences were incorrectly classified
(further treatment was improperly not recommended) and 3% of the
non-recurrences were incorrectly classified (further treatment was
incorrectly recommended).
[0060] As shown by this example, it may be feasible to attain a
higher instance of correctly identifying those patients who can
avoid the post-surgical cancer treatment regimes, using the
exemplary optimal categorization method 300. Even though the cutoff
point determined by the optimal categorization method 300 yielded a
moderately higher percentage of incorrectly classified recurrences,
it yielded a significantly lower percentage of incorrectly
classified non-recurrences. Thus, considering the trade-off, and
realizing that the goal of the optimization problem was the
avoidance of unnecessary treatment, the results of the cutoff point
determined by the optimal categorization method 300 are
mathematically superior to those of the prior art clinical cutoff
point. This type of information is potentially extremely useful in
providing additional insight to patients weighing the choice
between undergoing treatments such as chemotherapy or risking a
recurrence of breast cancer.
[0061] FIG. 9 is a comparison of exemplary post-processed output
from a first support vector machine comprising a linear kernel and
a second support vector machine comprising a polynomial kernel.
FIG. 9 demonstrates that a variation in the selection of a kernel
may affect the level of quality of the output of a SVM. As shown,
the post-processed output of a first SVM 902 comprising a linear
dot product kernel indicates that for a given test set of twenty
four sample, six of eight positive samples were incorrectly
identified and three of sixteen negative samples were incorrectly
identified. By way of comparison, the post-processed output for a
second SVM 904 comprising a polynomial kernel indicates that for
the same test set only two of eight positive samples were
incorrectly identified and four of sixteen negative samples were
identified. By way of comparison, the polynomial kernel yielded
significantly improved results pertaining to the identification of
positive samples and yielded only slightly worse results pertaining
to the identification of negative samples. Thus, as will be
apparent to those of skill in the art, the global minimum error for
the polynomial kernel is lower than the global minimum error for
the linear kernel for this data set.
[0062] FIG. 10 and the following discussion are intended to provide
a brief and general description of a suitable computing environment
for implementing the present invention. Although the system shown
in FIG. 10 is a conventional personal computer 1000, those skilled
in the art will recognize that the invention also may be
implemented using other types of computer system configurations.
The computer 1000 includes a central processing unit 1022, a system
memory 1020, and an Input/Output ("I/O") bus 1026. A system bus
1021 couples the central processing unit 1022 to the system memory
1020. A bus controller 1023 controls the flow of data on the I/O
bus 1026 and between the central processing unit 1022 and a variety
of internal and external 110 devices. The 1/0 devices connected to
the I/O bus 1026 may have direct access to the system memory 1020
using a Direct Memory Access ("DMA") controller 1024.
[0063] The I/O devices are connected to the I/O bus 1026 via a set
of device interfaces. The device interfaces may include both
hardware components and software components. For instance, a hard
disk drive 1030 and a floppy disk drive 1032 for reading or writing
removable media 1050 may be connected to the I/O bus 1026 through
disk drive controllers 1040. An optical disk drive 1034 for reading
or writing optical media 1052 may be connected to the I/O bus 1026
using a Small Computer System Interface ("SCSI") 1041.
Alternatively, an IDE (ATAPI) or EIDE interface may be associated
with an optical drive such as a may be the case with a CD-ROM
drive. The drives and their associated computer-readable media
provide nonvolatile storage for the computer 1000. In addition to
the computer-readable media described above, other types of
computer-readable media may also be used, such as ZIP drives, or
the like.
[0064] A display device 1053, such as a monitor, is connected to
the I/O bus 1026 via another interface, such as a video adapter
1042. A parallel interface 1043 connects synchronous peripheral
devices, such as a laser printer 1056, to the I/O bus 1026. A
serial interface 1044 connects communication devices to the I/O bus
1026. A user may enter commands and information into the computer
1000 via the serial interface 1044 or by using an input device,
such as a keyboard 1038, a mouse 1036 or a modem 1057. Other
peripheral devices (not shown) may also be connected to the
computer 1000, such as audio input/output devices or image capture
devices.
[0065] A number of program modules may be stored on the drives and
in the system memory 1020. The system memory 1020 can include both
Random Access Memory ("RAM") and Read Only Memory ("ROM"). The
program modules control how the computer 1000 functions and
interacts with the user, with I/O devices or with other computers.
Program modules include routines, operating systems 1065,
application programs, data structures, and other software or
firmware components. In an illustrative embodiment, the present
invention may comprise one or more pre-processing program modules
1075A, one or more post-processing program modules 1075B, and/or
one or more optimal categorization program modules 1077 and one or
more SVM program modules 1070 stored on the drives or in the system
memory 1020 of the computer 1000. Specifically, pre-processing
program modules 1075A, post-processing program modules 1075B,
together with the SVM program modules 1070 may comprise
computer-executable instructions for pre-processing data and
post-processing output from a learning machine and implementing the
learning algorithm according to the exemplary methods described
with reference to FIGS. 1 and 2. Furthermore, optimal
categorization program modules 1077 may comprise
computer-executable instructions for optimally categorizing a data
set according to the exemplary methods described with reference to
FIG. 3.
[0066] The computer 1000 may operate in a networked environment
using logical connections to one or more remote computers, such as
remote computer 1060. The remote computer 1060 may be a server, a
router, a peer device or other common network node, and typically
includes many or all of the elements described in connection with
the computer 1000. In a networked environment,-program modules and
data may be stored on the remote computer 1060. The logical
connections depicted in FIG. 10 include a local area network
("LAN") 1054 and a wide area network ("WAN") 1055. In a LAN
environment, a network interface 1045, such as an Ethernet adapter
card, can be used to connect the computer 1000 to the remote
computer 1060. In a WAN environment, the computer 1000 may use a
telecommunications device, such as a modem 1057, to establish a
connection. It will be appreciated that the network connections
shown are illustrative and other devices of establishing a
communications link between the computers may be used.
[0067] FIG. 11 is a functional block diagram illustrating an
alternate exemplary operating environment for implementation of the
present invention. The present invention may be implemented in a
specialized configuration of multiple computer systems. An example
of a specialized configuration of multiple computer systems is
referred to herein as the BIOWulf.TM. Support Vector Processor
(BSVP). The BSVP combines the latest advances in parallel computing
hardware technology with the latest mathematical advances in
pattern recognition, regression estimation, and density estimation.
While the combination of these technologies is a unique and novel
implementation, the hardware configuration is based upon Beowulf
supercomputer implementations pioneered by the NASA Goddard Space
Plight Center.
[0068] The BSVP provides the massively parallel computational power
necessary to expedite SVM training and evaluation on large-scale
data sets. The BSVP includes a dual parallel hardware architecture
and custom parallelized software to enable efficient utilization of
both multithreading and message passing to efficiently identify
support vectors in practical applications. Optimization of both
hardware and software enables the BSVP to significantly outperform
typical SVM implementations. Furthermore, as commodity computing
technology progresses the upgradability of the BSVP is ensured by
its foundation in open source software and standardized interfacing
technology. Future computing platforms and networking technology
can be assimilated into the BSVP as they become cost effective with
no effect on the software implementation.
[0069] As shown in FIG. 11, the BSVP comprises a Beowulf class
supercomputing cluster with twenty processing nodes 1104a-t and one
host node 1112. The processing nodes 1104a-j are interconnected via
switch 1102a, while the processing nodes 1104k-t are interconnected
via switch 1102b. Host node 1112 is connected to either one of the
network switches 1102a or 1102b (1102a shown) via an appropriate
Ethernet cable 1114. Also, switch 1102a and switch 1102b are
connected to each other via an appropriate Ethernet cable 1114 so
that all twenty processing nodes 1104a-t and the host node 1112 are
effectively in communication with each other. Switches 1102a and
1102b preferably comprise Fast Ethernet interconnections. The dual
parallel architecture of the BSVP is accomplished through
implementation of the Beowulf supercomputer's message passing
multiple machine parallel configuration and utilizing a high
performance dual processor SMP computer as the host node 1112.
[0070] In this exemplary configuration, the host node 1112 contains
glueless multi-processor SMP technology and consists of a dual 450
Mhz Pentium II Xeon based machine with 18 GB of Ultra SCSI storage,
256 MB memory, two 100 Mbit/sec NIC's, and a 24 GB DAT network
backup tape device. The host node 1112 executes NIS, MPL and/or PMV
under Linux to manage the activity of the BSVP. The host node 1112
also provides the gateway between the BSVP and the outside world.
As such, the internal network of the BSVP is isolated from outside
interaction, which allows the entire cluster to appear to function
as a single machine.
[0071] The twenty processing nodes 1104a-t are identically
configured computers containing 150 MHz Pentium processors, 32 MB
RAM, 850 MB HDD, 1.44 MB FDD, and a Fast Ethernet mb 100 Mb/s NIC.
The processing nodes 1104a-t are interconnected with each other and
the host node through NFS connections over TCP/IP. In addition to
BSVP computations, the processing nodes are configured to provide
demonstration capabilities through an attached bank of monitors
with each node's keyboard and mouse routed to a single keyboard
device and a single mouse device through the KVM switches 1108a and
1108b.
[0072] Software customization and development allow optimization of
activities on the BSVP. Concurrency in sections of SVM processes is
exploited in the most advantageous manner through the hybrid
parallelization provided by the BSVP hardware. The software
implements full cycle support from raw data to implemented
solution. A database engine provides the storage and flexibility
required for pre-processing raw data. Custom developed routines
automate the pre-processing of the data prior to SVM training.
Multiple transformations and data manipulations are performed
within the database environment to generate candidate training
data
[0073] The peak theoretical processing capability of the BSVP is
3.90 GFLOPS. Based upon the benchmarks performed by NASA Goddard
Space Flight Center on their Beowulf class machines, the expected
actual performance should be about 1.56 GFLOPS. Thus the
performance attained using commodity component computing power in
this Beowulf class cluster machine is in line with that of
supercomputers such as the Cray J932/8. Further Beowulf testing at
research and academic institutions indicates that a performance on
the order of 18 times a single processor can generally be attained
on a twenty node Beowulf cluster. For example, an optimization
problem requiring 17 minutes and 45 seconds of clock time on a
single Pentium processor computer was solved in 59 seconds on a
Beowulf with 20 nodes. Therefore, the high performance nature of
the BSVP enables practical analysis of data sets currently
considered too cumbersome to handle by conventional computer
systems.
[0074] The massive computing power of the BSVP renders it
particularly useful for implementing multiple SVMs in parallel to
solve real-life problems that involve a vast number of inputs.
Examples of the usefulness of SVMs in general and the BSVP in
particular comprise: genetic research, in particular the Human
Genome Project; evaluation of managed care efficiency; therapeutic
decisions and follow up; appropriate therapeutic triage;
pharmaceutical development techniques; discovery of molecular
structures; prognostic evaluations; medical informatics; billing
fraud detection; inventory control; stock evaluations and
predictions; commodity evaluations and predictions; and insurance
probability estimates.
[0075] Those skilled in the art should appreciate that the BSVP
architecture described above is illustrative in nature and is not
meant to limit the scope of the present invention. For example, the
choice of twenty processing nodes was based on the well known
Beowulf architecture. However, the BSVP may alternately be
implemented using more or less than twenty processing nodes.
Furthermore the specific hardware and software components recited
above are by way of example only. As mentioned, the BSVP embodiment
of the present invention is configured to be compatible with
alternate and/or future hardware and software components.
[0076] FIG. 12 is a functional block diagram illustrating an
exemplary network operating environment for implementation of a
further alternate embodiment of the present invention. In the
exemplary network operating environment, a customer 1202 or other
entity may transmit data via a distributed computer network, such
as the Internet 1204, to a vendor 1212. Those skilled in the art
should appreciate that the customer 1202 may transmit data from any
type of computer or lab instrument that includes or is in
communication with a communications device and a data storage
device. The data transmitted from the customer 1202 may be training
data, test data and/or live data to be processed by a learning
machine. The data transmitted by the customer is received at the
vendor's web server 1206, which may transmit the data to one or
more learning machines via an internal network 1214a-b. As
previously described, learning machines may comprise SVMs, BSVPs
1100, neural networks, other learning machines or combinations
thereof. Preferable, the web server 1206 is isolated from the
learning machine(s) by way of a firewall 1208 or other security
system. The vendor 1212 may also be in communication with one or
more financial institutions 1210, via the Internet 1204 or any
dedicated or on-demand communications link. The web server 1206 or
other communications device may handle communications with the one
or more financial institutions. The financial institution(s) may
comprise banks, Internet banks, clearing houses, credit or debit
card companies, or the like.
[0077] In operation, the vendor may offer learning machine
processing services via a web-site hosted at the web-server 1206 or
another server in communication with the web-server 1206. A
customer 1202 may transmit data to the web server 1206 to be
processed by a learning machine. The customer 1202 may also
transmit identification information, such as a username, a password
and/or a financial account identifier, to the web-server. In
response to receiving the data and the identification information,
the web server 1206 may electronically withdraw a pre-determined
amount of funds from a financial account maintained or authorized
by the customer 1202 at a financial institution 1210. In addition,
the web server may transmit the customer's data to the BSVP 1100 or
other learning machine. When the BSVP 1100 has completed processing
of the data and post-processing of the output, the post-processed
output is returned to the web-server 1206. As previously described,
the output from a learning machine may be post-processed in order
to generate a single-valued or multi-valued, computationally
derived alpha-numerical classifier, for human or automated
interpretation. The web server 1206 may then ensure that payment
from the customer has been secured before the post-processed output
is transmitted back to the customer 1202 via the Internet 1204.
[0078] SVMs may be used to solve a wide variety of real-life
problems. For example, SVMs may have applicability in analyzing
accounting and inventory data, stock and commodity market data,
insurance data, medical data, etc. As such, the above-described
network environment has wide applicability across many industries
and market segments. In the context of inventory data analysis, for
example, a customer may be a retailer. The retailer may supply
inventory and audit data to the web server 1206 at predetermined
times. The inventory and audit data may be processed by the BSVP
and/or one or more other learning machine in order to evaluate the
inventory requirements of the retailer. Similarly, in the context
of medical data analysis, the customer may be a medical laboratory
and may transmit live data collected from a patient to the web
server 1206 while the patient is present in the medical laboratory.
The output generated by processing the medical data with the BSVP
or other learning machine may be transmitted back to the medical
laboratory and presented to the patient.
[0079] Alternative embodiments of the present invention will become
apparent to those having ordinary skill in the art to which the
present invention pertains. Such alternate embodiments are
considered to be encompassed within the spirit and scope of the
present invention. Accordingly, the scope of the present invention
is described by the appended claims and is supported by the
foregoing description.
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