A new multi-class support vector algorithm

Multi-class classification is an important and on-going research subject in machine learning. In this article, we propose a new support vector algorithm, called nu-K-SVCR, for multi-class classification based on nu-support vector machine. nu-K-SVCR has parameters that enable us to control the numbers of support vectors and margin errors effectively, which is helpful in improving the accuracy of each classifier. We give some theoretical results concerning the significance of the parameters and show the robustness of classifiers. In addition, we have examined the proposed algorithm on several benchmark data sets and artificial data sets, and our preliminary experiments confirm our theoretical conclusions.