Application of the cross-entropy method to dual Lagrange support vector machine

Abstract

In this paper, cross entropy method is used for solving dual Lagrange support vector machine (SVM). Cross entropy (CE) method is a new practical approach which is widely used in some applications such as combinatorial optimization, learning algorithm and simulation. Our approach refers to Kernel Adatron which is solving dual Lagrange SVM using gradient ascent method. Hereby, the cross entropy method is applied to solve dual Lagrange SVM optimization problem to find the optimal or at least near optimal Lagrange multipliers as a solution. As known, the standard SVM with quadratic programming solver suffers from high computational time. Some real world datasets are used to test the algorithms and compare to the existing approach in terms of computation time and accuracy. Our approach is fast and produce good results in terms of generalization error. © 2009 Springer.