Patent ID: 7283984
Filing Date: 2007-10-16
Classification: G06K,G06N

Abstract:
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.