An improved working set selection method for SVM decomposition method

The support vector machine learning problem is a convex quadratic programming problem. For large learning tasks with many training examples, the general quadratic programs quickly become intractable in their memory and time requirements. Thus the decomposition method is essential for the support vector machine learning. The working set selection is the most important issue of the decomposition method. Convergence of problems depends on the working set selection. We propose a working set selection method that can be applicable to large working set. Experimental results on various problems show that the proposed method outperforms the existing methods.