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The Bounds on the Rate of Uniform Convergence for Learning Machine

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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

The generalization performance is the important property of learning machines. The desired learning machines should have the quality of stability with respect to the training samples. We consider the empirical risk minimization on the function sets which are eliminated noisy. By applying the Kutin’s inequality we establish the bounds of the rate of uniform convergence of the empirical risks to their expected risks for learning machines and compare the bounds with known results.

Supported in part by NSFC under grant 10371033.

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Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, Hubei University, Wuhan, 430062, China

    Bin Zou & Luoqing Li

  2. College of Computer Science, Huazhong University of Science and Technology, Wuhan, 430074, China

    Jie Xu

Authors
  1. Bin Zou
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  2. Luoqing Li
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  3. Jie Xu
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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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Zou, B., Li, L., Xu, J. (2005). The Bounds on the Rate of Uniform Convergence for Learning Machine. 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_86

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  • DOI: https://doi.org/10.1007/11427391_86

  • 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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