A Novel Sequential Minimal Optimization Algorithm for Support Vector Regression

Jun Guo, Norikazu Takahashi, Tetsuo Nishi

Abstract: A novel sequential minimal optimization (SMO) algorithm for support vector regression is proposed. This algorithm is based on Flake and Lawrence’s SMO in which convex optimization problems with l variables are solved instead of standard quadratic programming problems with 2l variables where l is the number of training samples, but the strategy for working set selection is quite different. Experimental results show that the proposed algorithm is much faster than Flake and Lawrence’s SMO and comparable to the fastest conventional SMO.