Kernel Selection for the Support Vector Machine

Rameswar DEBNATH  Haruhisa TAKAHASHI  

IEICE TRANSACTIONS on Information and Systems   Vol.E87-D    No.12    pp.2903-2904
Publication Date: 2004/12/01
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Biocybernetics, Neurocomputing
support vector machine,  feature space,  normalization,  kernel function,  

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The choice of kernel is an important issue in the support vector machine algorithm, and the performance of it largely depends on the kernel. Up to now, no general rule is available as to which kernel should be used. In this paper we investigate two kernels: Gaussian RBF kernel and polynomial kernel. So far Gaussian RBF kernel is the best choice for practical applications. This paper shows that the polynomial kernel in the normalized feature space behaves better or as good as Gaussian RBF kernel. The polynomial kernel in the normalized feature space is the best alternative to Gaussian RBF kernel.

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