SVM model selection with the VC bound

Authors:
Huaqing Li;Shaoyu Wang;Feihu Qi
Affiliations:
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, P. R. China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, P. R. China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, P. R. China
Venue:
CIS'04 Proceedings of the First international conference on Computational and Information Science
Year:
2004

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Abstract

Model selection plays a key role in the performance of a support vector machine (SVM). In this paper, we propose two algorithms that use the Vapnik Chervonenkis (VC) bound for SVM model selection. The algorithms employ a coarse-to-fine search strategy to obtain the best parameters in some predefined ranges for a given problem. Experimental results on several benchmark datasets show that the proposed hybrid algorithm has very comparative performance with the cross validation algorithm.