Benders decomposition technique for support vector regression
Theodore B. Trafali̇s, Hüseyin İnce · 2003
The theory of the support vector machine (SVM) algorithm is based on the statistical learning theory. Training of SVMs leads to either a quadratic programming (QP) problem, or linear programming (LP) problem. This depends on the specific norm that is used when the distance between the convex hulls of two classes are computed. The l/sub 1/ norm distance leads to a large scale linear programming problem in the case where the sample size is very large. We propose to apply the Benders decomposition technique to the resulting LP for the regression case. Preliminary results show that this technique is much faster than the QP formulation.