Automatic model selection method for support vector machines classifiers

An optimal approach was presented for model parameters of a support vector machine classifier based on coarse grid search combined with pattern search, in which the Jaakkola-Haussler error bound was considered as the evaluation criterion of model selection. Based on the Riemannian geometry theory, a novel conformal transformation was proposed and the kernel function was modified by the transformation in a data-dependent way. Simulated results for the artificial data set showed that the approach for automatic model selection was very effective. An application of the approach in handwritten similar Chinese characters recognition was further investigated. The experimental result showed remarkable improvement of the performance of the classifier.