Choosing multiple parameters for support vector machines

The problem of automatically tuning multiple parameters for pattern recogni tion Support Vector Machines (SVMs) is considered. This is done by minimizi ng some estimates of the generalization error of SVMs using a gradient desc ent algorithm over the set of parameters. Usual methods for choosing parame ters, based on exhaustive search become intractable as soon as the number o f parameters exceeds two. Some experimental results assess the feasibility of our approach for a large number of parameters (more than 100) and demons trate an improvement of generalization performance.