Pairwise multi-classification support vector machines: quadratic programming (QP-PAMSVM) formulations

Authors:
Theodore B. Trafalis;Olutayo Oladunni
Affiliations:
School of Industrial Engineering, University of Oklahoma, Norman, OK;School of Industrial Engineering, University of Oklahoma, Norman, OK
Venue:
NN'05 Proceedings of the 6th WSEAS international conference on Neural networks
Year:
2005

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Abstract

The binary support vector machines (SVMs) have been extensively investigated. However their extension to a multi-classification model is still an on-going research. In this paper we present an extension of the binary support vector machines (SVMs) for the k 2 class problems. The SVM model as originally proposed requires the construction of several binary SVM classifiers to solve the multi-class problem. We propose a single quadratic optimization problem called a pairwise multi-classification support vector machines (PAMSVMs) for constructing a pairwise linear and nonlinear classification decision functions. A kernel approach is also discussed for nonlinear classification problems. Computational results are presented for two real data sets.