Method and apparatus for optimizing support vector machine kernel parameters
First Claim
1. A method for optimizing support vector machine (SVM) kernel parameters, comprising:
- assigning sets of kernel parameter values to each node in a multiprocessor system;
performing a cross-validation operation at each node in the multiprocessor system based on a data set, wherein the cross-validation operation computes an error cost value reflecting the number of misclassifications that arise while classifying the data set using the assigned set of kernel parameter values;
communicating the computed error cost values between nodes in the multiprocessor system;
eliminating nodes with relatively high error cost values;
performing a cross-over operation in which kernel parameter values are exchanged between remaining nodes to produce new sets of kernel parameter values;
repeating the cross-validating, communicating eliminating, and cross-over operations until a global winning set of kernel parameter values is determined;
producing the global winning set of kernel parameters; and
using the kernel parameters in the kernel function of the SVM to map the data set from a low-dimensional input space to a higher-dimensional feature space.
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Abstract
One embodiment of the present invention provides a system that optimizes support vector machine (SVM) kernel parameters. During operation, the system assigns sets of kernel parameter values to each node in a multiprocessor system. Next, the system performs a cross-validation operation at each node in the multiprocessor system based on a data set. This cross-validation operation computes an error cost value reflecting the number of misclassifications that arise while classifying the data set using the assigned set of kernel parameter values. The system then communicates the computed error cost values between nodes in the multiprocessor system, and eliminates nodes with relatively high error cost values. Next, the system performs a cross-over operation in which kernel parameter values are exchanged between remaining nodes to produce new sets of kernel parameter values. This process is repeated until a global winning set of kernel parameter values emerges.
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Citations
17 Claims
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1. A method for optimizing support vector machine (SVM) kernel parameters, comprising:
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assigning sets of kernel parameter values to each node in a multiprocessor system; performing a cross-validation operation at each node in the multiprocessor system based on a data set, wherein the cross-validation operation computes an error cost value reflecting the number of misclassifications that arise while classifying the data set using the assigned set of kernel parameter values; communicating the computed error cost values between nodes in the multiprocessor system; eliminating nodes with relatively high error cost values; performing a cross-over operation in which kernel parameter values are exchanged between remaining nodes to produce new sets of kernel parameter values; repeating the cross-validating, communicating eliminating, and cross-over operations until a global winning set of kernel parameter values is determined; producing the global winning set of kernel parameters; and using the kernel parameters in the kernel function of the SVM to map the data set from a low-dimensional input space to a higher-dimensional feature space. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for optimizing support vector machine (SVM) kernel parameters, the method comprising:
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assigning sets of kernel parameter values to each node in a multiprocessor system; performing a cross-validation operation at each node in the multiprocessor system based on a data set, wherein the cross-validation operation computes an error cost value reflecting the number of misclassifications that arise while classifying the data set using the assigned set of kernel parameter values; communicating the computed error cost values between nodes in the multiprocessor system; eliminating nodes with relatively high error cost values; performing a cross-over operation in which kernel parameter values are exchanged between remaining nodes to produce new sets of kernel parameter values; repeating the cross-validating, communicating, eliminating, and cross-over operations until a global winning set of kernel parameter values is determined; producing the global winning set of kernel parameters; and using the kernel parameters in the kernel function of the SVM to map the data set from a low-dimensional input space to a higher-dimensional feature space. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. An apparatus that optimizes support vector machine (SVM) kernel parameters, comprising:
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an assignment mechanism configured to assign sets of kernel parameter values to each node in a multiprocessor system; a cross-validation mechanism configured to perform a cross-validation operation at each node in the multiprocessor system based on a data set, wherein the cross-validation mechanism computes an error cost value reflecting the number of misclassifications that arise while classifying the data set using the assigned set of kernel parameter values; a communication mechanism configured to communicate the computed error cost values between nodes in the multiprocessor system; an elimination mechanism configured to eliminate nodes with relatively high error cost values; a cross-over mechanism configured to perform a cross-over operation in which kernel parameter values are exchanged between remaining nodes to produce new sets of kernel parameter values, wherein the apparatus continues iteratively producing, cross-validating and eliminating sets of kernel parameter values until a global winning set of kernel parameter values emerges; a production mechanism configured to produce the global winning set of kernel parameters; and a computation mechanism configured to use the kernel parameters in the kernel function of the SVM to map the data set from a low-dimensional input space to a higher-dimensional feature space. - View Dependent Claims (16, 17)
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Specification