Fast Rates for Support Vector Machines

Ingo Steinwart, Clint Scovel

Abstract: We establish learning rates to the Bayes risk for support vector machines (SVMs) using a regularization sequence ${\it \lambda}_{n}={\it n}^{-\rm \alpha}$ , where ${\it \alpha}\in$ (0,1) is arbitrary. Under a noise condition recently proposed by Tsybakov these rates can become faster than n − 1/2. In order to deal with the approximation error we present a general concept called the approximation error function which describes how well the infinite sample versions of the considered SVMs approximate the data-generating distribution. In addition we discuss in some detail the relation between the “classical” approximation error and the approximation error function. Finally, for distributions satisfying a geometric noise assumption we establish some learning rates when the used RKHS is a Sobolev space.