nips nips2004 nips2004-69 nips2004-69-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: 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.. 1
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