Using Analytic QP and Sparseness to Speed Training of Support Vector Machines

Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) problem. This paper proposes an al(cid:173) gorithm for training SVMs: Sequential Minimal Optimization, or SMO. SMO breaks the large QP problem into a series of smallest possible QP problems which are analytically solvable. Thus, SMO does not require a numerical QP library. SMO's computation time is dominated by eval(cid:173) uation of the kernel, hence kernel optimizations substantially quicken SMO.