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Kernel Width Selection for SVM Classification

Published in IGI Global eBooks • Jan 1, 2008
NobleIDNI6P56W16R85S48
Authors:
Shawkat Ali
,
Kate Smith‐Miles

Abstract

The most critical component of kernel based learning algorithms is the choice of an appropriate kernel and its optimal parameters. In this paper we propose a rule based meta-learning approach for automatic radial basis function (rbf) kernel and its parameter selection for Support Vector Machine (SVM...

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