Trajectory-Based support vector multicategory classifier

Authors:
Daewon Lee;Jaewook Lee
Affiliations:
Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Kyungbuk, Korea;Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Kyungbuk, Korea
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

Citing 6
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Abstract

Support vector machines are primarily designed for binary-class classification. Multicategory classification problems are typically solved by combining several binary machines. In this paper, we propose a novel classifier with only one machine for even multiclass data sets. The proposed method consists of two phases. The first phase builds a trained kernel radius function via the support vector domain decomposition. The second phase constructs a dynamical system corresponding to the trained kernel radius function to decompose data domain and to assign class label to each decomposed domain. Numerical results show that our method is robust and efficient for multicategory classification.