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Sparse Least Squares Support Vector Machine for Function Estimation

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3971))

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Abstract

Sparse least squares support vector machine (SLS-SVM) for regression is proposed to solve the problem of regression for large sample data. The samples are mapped into Reproducing Kernel Hilbert Space (RKHS) and span a subspace there. Then we could find the basis of the subspace. The basis can represent all the samples linearly. So we can get the least squares support vector machine by solving a small equations set. A numerical example is used to illustrate that this approach can be used to fit nonlinear models for large data set. Being compared with common least squares support vector machine, this method can find sparse solution without any pruning or surgeon, and the computing speed is much faster because the final result is found by solving a small-scale equations set.

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Author information

Authors and Affiliations

  1. National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China

    Liang-zhi Gan & You-xian Sun

  2. Electrical Engineering Department, Xuzhou Normal University, Xuzhou, 221011, China

    Liang-zhi Gan & Hai-kuan Liu

Authors
  1. Liang-zhi Gan
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  2. Hai-kuan Liu
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  3. You-xian Sun
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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. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Gan, Lz., Liu, Hk., Sun, Yx. (2006). Sparse Least Squares Support Vector Machine for Function Estimation. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_149

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  • DOI: https://doi.org/10.1007/11759966_149

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34439-1

  • Online ISBN: 978-3-540-34440-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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