Fast Rates to Bayes for Kernel Machines

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

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Ingo Steinwart, Clint Scovel


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..