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Home > Archive>Volume 14, Issue 12, 2003 >2006-2013
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An Improved Sequential Minimal Optimization Learning Algorithm for Regression Support Vector Machine
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    Abstract:

    Support vector machine (SVM) is a learning technique based on the structural risk minimization principle, and it is also a class of regression method with a good generalization ability. This paper presents an improved SMO (sequential minimal optimization) algorithm to train the regression SVM, which gives an the analytical solution to the QP problem of size two. A new working set selection method and a stopping condition are developed. The simulation results show that the improved SMO algorithm is significantly faster and more precise than the original SMO one.

    Reference
    [1]Vapnik VN. The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
    [2]Cherkassky V, Mulier F. Learning from Data-Concepts, Theory and Methods. New York: John Wiley Sons, 1998.
    [3]Joachims T. Text categorization with support vector machines: Learning with many relevant features. In: Proceedings of the European Conference on Machine Learning (ECML). Berlin: Springer-Verlag, 1998. 37～142.
    [4]Weston GJ, Barnhill S. Gene selection for cancer classification using support vector machines. Machine Learning, 2002,46(1-3): 389～422.
    [5]Platt JC. Fast training of support vector machines using sequential minimal optimization. In: Scholkopf B, Burges C, Smola A, eds. Advances in Kernel Methods: Support Vector Machines. Cambridge: MIT Press, 1998. 185～208.
    [6]Smola AJ. Learning with kernels [Ph.D. Thesis]. University of Birlinghoven, 1998.
    [7]Smola AJ, Scholkopf B. A tutorial on support vector regression. Technical Report, TR 1998-030. London: Royal Holloway College, 1998.
    [8]Shevade SK, Keerthi SS, Bhattacharyya C. Improvements to SMO algorithm for SVM regression. IEEE Transactions on Neural Networks, 2000,11(5):1188～1194.
    [9]Flake GW, Lawrence S. Efficient SVM regression training with SMO. Machine Learning Special Issue on SVMs, 2000,46(1～3): 271～290.
    [10]Martin M. On-Line support vector machines for function approximation. Technical Report, LSI-02-11-R. Catalunya: Department of Software, Universitat Politecnica de Catalunya, 2002.
Get Citation

张浩然,韩正之.回归支持向量机的改进序列最小优化学习算法.软件学报,2003,14(12):2006-2013

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History
  • Received:November 04,2002
  • Revised:March 04,2003
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