SMO Algorithms for Support Vector Machines without Bias Term
Michael Vogt · TUbilio (Technical University of Darmstadt) · 2002
In the original algorithm, two parameters have to be optimized per step to ensure that the solution obeys the summation constraint. The algorithm adapted to the problem defined above sequentially optimizes only one parameter αi per step, which makes it significantly easier and faster: On the one hand, the analytical solution is easier to compute for a one-dimensional problem. On the other hand, we do not need an (occasionally) time-consuming selection of the second parameter. The parameter to optimize is simply chosen by Platt’s “first choice heuristic”. The absence of the “second choice heuristic” raises the question, if an error cache (or function cache) is needed. However, simulations have shown that a cache speeds up the algorithm, since the cache values can also made use of for checking the KKT conditions.