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Journal Article

Choosing Multiple Parameters for Support Vector Machines

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Chapelle, O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Vapnik V, Bousquet, O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Chapelle, O., Vapnik V, Bousquet, O., & Mukherjee, S. (2002). Choosing Multiple Parameters for Support Vector Machines. Machine Learning, 46(1), 131-159.

Abstract

The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVM) is considered. This is done by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters. Usual methods for choosing parameters, based on exhaustive search become intractable as soon as the number of parameters exceeds two. Some experimental results assess the feasibility of our approach for a large number of parameters (more than 100) and demonstrate an improvement of generalization performance.