A Fast Method for Training Linear SVM in the Primal
Trinh Minh Tri Do and Thierry Artieres
In: ECML PKDD 2008, 15 - 19 September 2008, Antwerp, Belgium.

## 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 $O(1/{\lambda\epsilon})$ iterations to converge to a solution with accuracy $\epsilon$. 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.

EPrint Type: Conference or Workshop Item (Paper) Project Keyword UNSPECIFIED Learning/Statistics & Optimisation 5088 Trinh Minh Tri Do 24 March 2009