Classifier complexity reduction by support vector pruning in kernel matrix learning

Authors:
V. Vijaya Saradhi;Harish Karnick
Affiliations:
Department of Computer Science and Engineering, Indian Institute of Technology, Kanpur, India;Department of Computer Science and Engineering, Indian Institute of Technology, Kanpur, India
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

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Abstract

This paper presents an algorithm for reducing a classifier's complexity by pruning support vectors in learning the kernel matrix. The proposed algorithm retains the 'best' support vectors such that the span of support vectors, as defined by Vapnik and Chapelle, is as small as possible. Experiments on real world data sets show that the number of support vectors can be reduced in some cases by as much as 85% with little degradation in generalization performance.