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International Conference on Natural Computation

ICNC 2005: Advances in Natural Computation pp 221–224Cite as

A Fast SMO Training Algorithm for Support Vector Regression

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  • 2006 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3610))

Abstract

Support vector regression (SVR) is a powerful tool to solve regression problem, this paper proposes a fast Sequential Minimal Optimization (SMO) algorithm for training support vector regression (SVR), firstly gives a analytical solution to the size two quadratic programming (QP) problem, then proposes a new heuristic method to select the working set which leads to algorithm’s faster convergence. The simulation results indicate that the proposed SMO algorithm can reduce the training time of SVR, and the performance of proposed SMO algorithm is better than that of original SMO algorithm.

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References

  1. Vapnik, V.N.: The nature of statistical learning theory. Springer, New York (1995)

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  2. Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. Advances in Kernel Methods: Support vector Machines. MIT Press, Cambridge (1998)

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Author information

Authors and Affiliations

  1. College of Information Science and Engineering, Zhejiang Normal University, Jinhua, 321004, China

    Haoran Zhang, Xiaodong Wang, Changjiang Zhang & Xiuling Xu

Authors
  1. Haoran Zhang
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  2. Xiaodong Wang
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  3. Changjiang Zhang
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  4. Xiuling Xu
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Cite this paper

Zhang, H., Wang, X., Zhang, C., Xu, X. (2005). A Fast SMO Training Algorithm for Support Vector Regression. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_26

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  • DOI: https://doi.org/10.1007/11539087_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28323-2

  • Online ISBN: 978-3-540-31853-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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