A Fast Method for Training Linear SVM in the Primal

author: Trinh Minh Tri Do, +Marie Curie Unit, DG Research, European Commission
author: Thierry Artières, LIP6, Université Pierre et Marie Curie - Paris 6
published: Oct. 10, 2008,   recorded: September 2008,   views: 4507

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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 $O(1/{lambdaepsilon})$ 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.

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