Claims
- 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.
Priority Claims (1)
Number |
Date |
Country |
Kind |
9-052608 |
Mar 1997 |
JP |
|
RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 60/083,961, filed May 1, 1998.
Provisional Applications (1)
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Number |
Date |
Country |
|
60083961 |
May 1998 |
US |
Continuations (2)
|
Number |
Date |
Country |
Parent |
09715832 |
Nov 2000 |
US |
Child |
10224707 |
Aug 2002 |
US |
Parent |
09305345 |
May 1999 |
US |
Child |
09715832 |
Nov 2000 |
US |