Domain described support vector classifier for multi-classification problems

Authors:
Daewon Lee;Jaewook Lee
Affiliations:
Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Kyungbuk 790-784, Republic of Korea;Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Kyungbuk 790-784, Republic of Korea
Venue:
Pattern Recognition
Year:
2007

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Abstract

In this paper, a novel classifier for multi-classification problems is proposed. The proposed classifier, based on the Bayesian optimal decision theory, tries to model the decision boundaries via the posterior probability distributions constructed from support vector domain description rather than to model them via the optimal hyperplanes constructed from two-class support vector machines. Experimental results show that the proposed method is more accurate and efficient for multi-classification problems.