Global convergence of SMO algorithm for support vector regression |
| |
Authors: | Norikazu Takahashi Jun Guo Tetsuo Nishi |
| |
Affiliation: | Department of Computer Science and Communication Engineering, Kyushu University, Fukuoka 819-0395, Japan. norikazu@csce.kyushu-u.ac.jp |
| |
Abstract: | Global convergence of the sequential minimal optimization (SMO) algorithm for support vector regression (SVR) is studied in this paper. Given l training samples, SVR is formulated as a convex quadratic programming (QP) problem with l pairs of variables. We prove that if two pairs of variables violating the optimality condition are chosen for update in each step and subproblems are solved in a certain way, then the SMO algorithm always stops within a finite number of iterations after finding an optimal solution. Also, efficient implementation techniques for the SMO algorithm are presented and compared experimentally with other SMO algorithms. |
| |
Keywords: | |
|
|