An Efficient Implementaion of an Active Set Method for SVM

We propse an active set algorithm to solve the convex quadratic programming (QP) problem which is the core of the support vector machine (SVM) training. The uderlying method is not new and is based on the extensive practice of the Simplex method and its variants for convex quadratic problems. However, its application to large-scale SVM problems is new. Until recently the traditional active set methods were considered impractical for large SVM problems. By adapting th emethods to the special structure of SVM problems we were ab le to produce an efficient implementation. We conduct an extensive study of the behavior of our method and its variations on SVM problems. We present computational results comparing our method with Joachims' SVM light [16]. The results show that our method has overall better performance on many SVM problems. It seems to have particularly strong advantage on more difficult problems. In addition this algorithm has better theoretical properties and it naturallyt extends to the incremental mode.

By: Katya Scheinberg

Published in: RC23774 in 2005


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