Support vector machines for multi-class pattern recognition.

Abstract

. The solution of binary classification problems using support vector machines (SVMs) is well developed, but multi-class problems with more than two classes have typically been solved by combining independently produced binary classifiers. We propose a formulation of the SVM that enables a multi-class pattern recognition problem to be solved in a single optimisation. We also propose a similar generalization of linear programming machines. We report experiments using bench-mark datasets in which these two methods achieve a reduction in the number of support vectors and kernel calculations needed.