We develop, analyze, and test a training algorithm for support vector machine classifiers without offset. Key features of this algorithm are a new stopping criterion and a set of inexpensive working set selection strategies that need almost as few iterations than the optimal working set selection strategy. For these working set strategies, we establish convergence rates that coincide with the best known rates for SVMs with offset. We further conduct various experiments that investigate both the run time behavior and the performed iterations of the new training algorithm. It turns out, that the new algorithm needs significantly less iterations and run-time than standard training algorithms for SVMs with offset.
I. Steinwart, D. Hush, and C. Scovel, Training SVMs without Offset. Los Alamos National Laboratory Technical Report LA-UR-09-00638. Submitted for publication, 2009. [ Abstract | PDF (641 KB) ]
