Equivalence of classification and regression under support vector machine theory

  • Authors:

  • Chunguo Wu;Yanchun Liang;Xiaowei Yang;Zhifeng Hao

  • Affiliations:

  • College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Changchun, China;College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Changchun, China;Department of Applied Mathematics, South China University of Technology, Guangzhou, China;Department of Applied Mathematics, South China University of Technology, Guangzhou, China

  • Venue:
  • ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
  • Year:
  • 2005

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Abstract

A novel classification method based on regression is proposed in this paper and then the equivalences of the classification and regression are demonstrated by using numerical experiments under the framework of support vector machine. The proposed algorithm implements the classification tasks by the way used in regression problems. It is more efficiently for multi-classification problems since it can classify all samples at a time. Numerical experiments show that the two classical machine learning problems (classification and regression) can be solved by the method conventionally used for the opposite problem and the proposed regression-based classification algorithm can classify all samples belonging to different categories concurrently with an agreeable precision.