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