Fast Rates to Bayes for Kernel Machines

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

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Authors

Ingo Steinwart, Clint Scovel

Abstract

We establish learning rates to the Bayes risk for support vector machines (SVMs) with hinge loss. In particular, for SVMs with Gaussian RBF kernels we propose a geometric condition for distributions which can be used to determine approximation properties of these kernels. Finally, we compare our methods with a recent paper of G. Blanchard et al..