Working Set Selection Using Second Order Information for Training Support Vector Machines

Rong-En Fan; Pai-Hsuen Chen; Chih-Jen Lin

Working set selection is an important step in decomposition methods for training support vector machines (SVMs). This paper develops a new technique for working set selection in SMO-type decomposition methods. It uses second order information to achieve fast convergence. Theoretical properties such as linear convergence are established. Experiments demonstrate that the proposed method is faster than existing selection methods using first order information.

Working Set Selection Using Second Order Information for Training Support Vector Machines

Abstract