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Optimal Gaussian Kernel Parameter Selection for SVM Classifier
IEICE TRANSACTIONS on Information and Systems Vol.E93-D No.12 pp.3352-3358
Publication Date: 2010/12/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Pattern Recognition
kernel parameter selection,
support vector machines,
Full Text: PDF(268.1KB)
The performance of the kernel-based learning algorithms, such as SVM, depends heavily on the proper choice of the kernel parameter. It is desirable for the kernel machines to work on the optimal kernel parameter that adapts well to the input data and the learning tasks. In this paper, we present a novel method for selecting Gaussian kernel parameter by maximizing a class separability criterion, which measures the data distribution in the kernel-induced feature space, and is invariant under any non-singular linear transformation. The experimental results show that both the class separability of the data in the kernel-induced feature space and the classification performance of the SVM classifier are improved by using the optimal kernel parameter.