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Fast Rates for Support Vector Machines Using Gaussian Kernels
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Journal Article
Fast Rates for Support Vector Machines Using Gaussian Kernels
2007
Overview
For binary classification we establish learning rates up to the order of n⁻¹ for support vector machines (SVMs) 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.
Publisher
Institute of Mathematical Statistics,The Institute of Mathematical Statistics
Subject
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