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Fast rates for support vector machines using Gaussian kernels. (English) Zbl 1127.68091
Summary: For binary classification we establish learning rates up to the order of \(n^{-1}\) for support vector machines with hinge loss and Gaussian RBF kernels. These rates are in terms of two assumptions on the considered distributions: Tsybakov’s noise assumption to establish a small estimation error, and a new geometric noise condition which is used to bound the approximation error. Unlike previously proposed concepts for bounding the approximation error, the geometric noise assumption does not employ any smoothness assumption.

MSC:
68T05 Learning and adaptive systems in artificial intelligence
41A46 Approximation by arbitrary nonlinear expressions; widths and entropy
62G20 Asymptotic properties of nonparametric inference
68T10 Pattern recognition, speech recognition
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