A new support vector machine for multi-class classification

Authors:
Zhiquan Qi;Yingjie Tian;Naiyang Deng
Affiliations:
College of Science, China Agricultural University, Beijing, China;Chinese Academy of Sciences Research Center on Data Technology & Knowledge Economy, Beijing, China;College of Science, China Agricultural University, Beijing, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

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Abstract

Support Vector Machines (SVMs) for classification – in short SVM – have been shown to be promising classification tools in many real-world problems. How to effectively extend binary SVC to multi-class classification is still an on-going research issue. In this article, instead of solving quadratic programming (QP) in Algorithm K-SVCR and Algorithm ν-K-SVCR, a linear programming (LP) problem is introduced in our algorithm. This leads to a new algorithm for multi-class problem, K-class Linear programming ν–Support Vector Classification-Regression(Algorithm ν-K-LSVCR). Numerical experiments on artificial data sets and benchmark data sets show that the proposed method is comparable to Algorithm K-SVCR and Algorithm ν-K-SVCR in errors, while considerably faster than them.