Least squares K-SVCR multi-class classification

Abstract

The support vector classification-regression machine for K-class classification (K-SVCR) is a novel multi-class classification method based on “1-versus-1-versus-rest” structure. In this paper, we propose a least squares version of K-SVCR named as LSK-SVCR. Similarly as the K-SVCR algorithm, this method assess all the training data into a “1-versus-1-versus-rest” structure, so that the algorithm generates ternary output $$ \{-1, 0, +1\}$$. In LSK-SVCR, the solution of the primal problem is computed by solving only one system of linear equations instead of solving the dual problem, which is a convex quadratic programming problem in K-SVCR. Experimental results on several benchmark data set show that the LSK-SVCR has better performance in the aspects of predictive accuracy and learning speed.