Choosing Multiple Parameters for Support Vector Machines

Authors:
Olivier Chapelle;Vladimir Vapnik;Olivier Bousquet;Sayan Mukherjee
Affiliations:
LIP6, Paris, France. olivier.chapelle@lip6.fr;AT&T Research Labs, 200 Laurel Ave, Middletown, NJ 07748, USA. vlad@research.att.com;École Polytechnique, France. bousquet@cmapx.polytechnique.fr;MIT, Cambridge, MA 02139, USA. sayan@ai.mit.edu
Venue:
Machine Learning
Year:
2002

Citing 9
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Abstract

The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) 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.