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Trajectory-Based Support Vector Multicategory Classifier

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3496))

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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.

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Author information

Authors and Affiliations

  1. Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Kyungbuk, 790-784, Korea

    Daewon Lee & Jaewook Lee

Authors
  1. Daewon Lee
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  2. Jaewook Lee
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiaofeng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Lee, D., Lee, J. (2005). Trajectory-Based Support Vector Multicategory Classifier. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_137

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  • DOI: https://doi.org/10.1007/11427391_137

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25912-1

  • Online ISBN: 978-3-540-32065-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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