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Minimal Enclosing Sphere Estimation and Its Application to SVMs Model Selection

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Advances in Neural Networks – ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3173))

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Abstract

In this paper, we propose a modified sequential minimal optimization (SMO) algorithm for the minimal enclosing sphere estimation (MESE) problem. Being one of the key issues of the VC dimension estimation, the MESE problem has a formulation similar to that of support vector machines (SVMs) training. This allows adoption of the ideas of Platt’s SMO algorithm. After careful analysis of the MESE problem, key issues are addressed. Experimental results show the feasibility and effectiveness of the proposed algorithm when applied to SVMs model selection.

This work is supported by the National Natural Science Foundation of China (No. 60072029).

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 20030, P.R. China

    Huaqing Li, Shaoyu Wang & Feihu Qi

Authors
  1. Huaqing Li
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  2. Shaoyu Wang
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  3. Feihu Qi
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

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

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Li, H., Wang, S., Qi, F. (2004). Minimal Enclosing Sphere Estimation and Its Application to SVMs Model Selection. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_81

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  • DOI: https://doi.org/10.1007/978-3-540-28647-9_81

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22841-7

  • Online ISBN: 978-3-540-28647-9

  • eBook Packages: Springer Book Archive

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