Some Aspects of Learning Rates for SVMs

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.

Author: Ingo Steinwart, Los Alamos National Laboratory