Active Set Iteration Method for New L2 Soft Margin Support Vector Machine

Abstract

This paper introduces a new L2 soft margin support vector machine (new L2 SVM). The dual problem for the constrained optimization of the SVM is a convex quadratic problem with simple bound constraints. The active set iteration method for this optimization problem is applied as a fast learning algorithm for the SVM, and the selection of the initial active/inactive sets is discussed. For incremental learning and large-scale learning problems, a fast incremental learning algorithm for the SVM is presented. Computational experiments show the efficiency of the proposed algorithms.