Convergence Proof of a Sequential Minimal Optimization Algorithm for Support Vector Regression
Jun Guo, Norikazu Takahashi, Tetsuo Nishi · The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006
A sequential minimal optimization (SMO) algorithm for support vector regression (SVR) has recently been proposed by Flake and Lawrence. However, the convergence of their algorithm has not been proved so far. In this paper, we consider an SMO algorithm, which deals with the same optimization problem as Flake and Lawrence's SMO, and give a rigorous proof that it always stops within a finite number of iterations.