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Method and apparatus for optimizing support vector machine kernel parameters

  • US 7,283,984 B1
  • Filed: 02/01/2005
  • Issued: 10/16/2007
  • Est. Priority Date: 02/01/2005
  • Status: Active Grant
First Claim
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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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