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A Novel Sequential Minimal Optimization Algorithm for Support Vector Regression

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Neural Information Processing (ICONIP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4232))

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Abstract

A novel sequential minimal optimization (SMO) algorithm for support vector regression is proposed. This algorithm is based on Flake and Lawrence’s SMO in which convex optimization problems with l variables are solved instead of standard quadratic programming problems with 2l variables where l is the number of training samples, but the strategy for working set selection is quite different. Experimental results show that the proposed algorithm is much faster than Flake and Lawrence’s SMO and comparable to the fastest conventional SMO.

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References

  1. Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)

    MATH  Google Scholar 

  2. Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods: Support Vector Machines. MIT Press, Cambridge (1998)

    Google Scholar 

  3. Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Technical Report NC2-TR-1998-030, NeuroCOLT2 (1998)

    Google Scholar 

  4. Shevade, S.K., Keerthi, S.S., Bhattacharyya, C., Murthy, K.R.K.: Improvements to the SMO algorithm for SVM regression. IEEE Trans. Neural Networks 11(5), 1188–1193 (2000)

    Article  Google Scholar 

  5. Flake, G.W., Lawrence, S.: Efficient SVM regression training with SMO. Machine Learning 46, 271–290 (2002)

    Article  MATH  Google Scholar 

  6. Guo, J., Takahashi, N., Nishi, T.: Convergence proof of a sequential minimal optimization algorithm for support vector regression. In: Proc. of IJCNN 2006 (2006)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Communication Engineering, Kyushu Univ., Fukuoka, 812-8581, Japan

    Jun Guo & Norikazu Takahashi

  2. Faculty of Science and Engineering, Waseda Univ., Tokyo, 162-0072, Japan

    Tetsuo Nishi

Authors
  1. Jun Guo
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  2. Norikazu Takahashi
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  3. Tetsuo Nishi
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, The Chinese Univ. of Hong Kong, Shatin, N.T., Hong Kong

    Irwin King

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

    Jun Wang

  3. The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Lai-Wan Chan

  4. Department of Computer Science and Engineering & Center for Cognitive Science, The Ohio State University, OH 43210, Columbus

    DeLiang Wang

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© 2006 Springer-Verlag Berlin Heidelberg

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Guo, J., Takahashi, N., Nishi, T. (2006). A Novel Sequential Minimal Optimization Algorithm for Support Vector Regression. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_92

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  • DOI: https://doi.org/10.1007/11893028_92

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46479-2

  • Online ISBN: 978-3-540-46480-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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