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
LIMITED DISTRIBUTION NOTICE:
This Research Report is available. This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g., payment of royalties). I have read and understand this notice and am a member of the scientific community outside or inside of IBM seeking a single copy only.
Questions about this service can be mailed to reports@us.ibm.com .