Some Aspects of Learning Rates for SVMs

author: Ingo Steinwart, Los Alamos National Laboratory
published: Feb. 25, 2007,   recorded: May 2005,   views: 5751


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We present some learning rates for support vector machine classification. In particular we discuss a recently proposed geometric noise assumption which allows to bound the approximation error for Gaussian RKHSs. Furthermore we show how a noise assumption proposed by Tsybakov can be used to obtain learning rates between 1/sqrt(n) and 1/n. Finally, we describe the influence of the approximation error on the overall learning rate.

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