A fast method for training linear SVM in the primal

Abstract

We propose a new algorithm for training a linear Support Vector Machine in the primal. The algorithm mixes ideas from non smooth optimization, subgradient methods, and cutting planes methods. This yields a fast algorithm that compares well to state of the art algorithms. It is proved to require O(1/λε) iterations to converge to a solution with accuracy ε. Additionally we provide an exact shrinking method in the primal that allows reducing the complexity of an iteration to much less than O(N) where N is the number of training samples. © 2008 Springer-Verlag Berlin Heidelberg.