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Article Navigation >  Journal of University of Electronic Science and Technology of China  > 2010  >  39(4): 523-527,555

ZHANG Hong-da, WANG Xiao-dan, XU Hai-long. Pseudo Gradient and Dynamic Step Optimization Algorithm for RBF-SVM Parameter Search[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(4): 523-527,555. doi: 10.3969/j.issn.1001-0548.2010.04.010
Citation: ZHANG Hong-da, WANG Xiao-dan, XU Hai-long. Pseudo Gradient and Dynamic Step Optimization Algorithm for RBF-SVM Parameter Search[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(4): 523-527,555. doi: 10.3969/j.issn.1001-0548.2010.04.010
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Pseudo Gradient and Dynamic Step Optimization Algorithm for RBF-SVM Parameter Search

doi: 10.3969/j.issn.1001-0548.2010.04.010
  • 1.

    Mailbox No. 25,Sanyuan Sanyuan Shanxi 713800

  • Received Date: 2009-01-04
  • Rev Recd Date: 2009-09-30
  • Publish Date: 2010-08-15
  • To the issue of hyper-parameter selection for radial basis function (RBF) based support vector machines (SVM), a new algorithm named as pseudo gradient and dynamic step optimization is proposed. Based on the characteristics of RBF, the kernel parameter is pre-estimated according to the distribution of the train set and the logarithmic scale is employed for the parameter space. The search direction is estimated with the changing of classification accuracy and by tuning the search step accordingly. At last, comparative experiments with Grid approach and PSO algorithm indicate the validity of the proposed algorithm.
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Pseudo Gradient and Dynamic Step Optimization Algorithm for RBF-SVM Parameter Search

doi: 10.3969/j.issn.1001-0548.2010.04.010
  • 1. Mailbox No. 25,Sanyuan Sanyuan Shanxi 713800
  • Received Date: 2009-01-04
  • Rev Recd Date: 2009-09-30
  • Publish Date: 2010-08-15

Abstract: To the issue of hyper-parameter selection for radial basis function (RBF) based support vector machines (SVM), a new algorithm named as pseudo gradient and dynamic step optimization is proposed. Based on the characteristics of RBF, the kernel parameter is pre-estimated according to the distribution of the train set and the logarithmic scale is employed for the parameter space. The search direction is estimated with the changing of classification accuracy and by tuning the search step accordingly. At last, comparative experiments with Grid approach and PSO algorithm indicate the validity of the proposed algorithm.

ZHANG Hong-da, WANG Xiao-dan, XU Hai-long. Pseudo Gradient and Dynamic Step Optimization Algorithm for RBF-SVM Parameter Search[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(4): 523-527,555. doi: 10.3969/j.issn.1001-0548.2010.04.010
Citation: ZHANG Hong-da, WANG Xiao-dan, XU Hai-long. Pseudo Gradient and Dynamic Step Optimization Algorithm for RBF-SVM Parameter Search[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(4): 523-527,555. doi: 10.3969/j.issn.1001-0548.2010.04.010
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