A fast iterative nearest point algorithm for support vector machine classifier design

In this paper we give a new, fast iterative algorithm for support vector machine (SVM) classifier design. The problem is converted to a problem of computing the nearest point between two convex polytopes. The suitability of two classical nearest point algorithms, due to Gilbert, and Mitchell, Dem'yanov and Malozemov, is studied. Ideas from both these algorithms are combined and modified to derive our fast algorithm. Comparitive computational evaluation of our algorithm against powerful SVM methods such as Platt's Sequential Minimal Optimization shows that our algorithm is very competitive.