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Improvement on Response Performance of Min-Max Modular Classifier by Symmetric Module Selection

  • Conference paper
Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3497))

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Abstract

This paper aims to improve the response performance of min-max modular classifier by a module selection policy for two-class classification during recognition. We propose an efficient base classifier selection algorithm. We show that the quadratic complexity of original min-max modular classifier can fall onto the level of linear complexity in the number of base-classifier modules for each input sample under presented selection scheme. The experimental results indicate the effectiveness of our algorithm and verify our theoretical analysis.

This work was supported in part by the National Natural Science Foundation of China via the grants NSFC 60375022 and NSFC 60473040.

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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, 1954 Huashan Rd., Shanghai, 200030, China

    Hai Zhao & Baoliang Lu

Authors
  1. Hai Zhao
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  2. Baoliang Lu
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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

    Xiao-Feng 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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Zhao, H., Lu, B. (2005). Improvement on Response Performance of Min-Max Modular Classifier by Symmetric Module Selection. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_7

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  • DOI: https://doi.org/10.1007/11427445_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25913-8

  • Online ISBN: 978-3-540-32067-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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