Journal of Systems Engineering and Electronics ›› 2011, Vol. 33 ›› Issue (8): 1820-1823.doi: 10.3969/j.issn.1001-506X.2011.08.27

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Combined parameter optimization for ε-SVR based on weighted accuracy

SUN Lin-kai, JIN Jia-shan, GENG Jun-bao   

  1. Department of Power Engineering, School of Naval Architecture & Power, Naval University of Engineering, Wuhan 430033, China
  • Online:2011-08-15 Published:2010-01-03

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Abstract:

Aiming at the lack of integrity theories for choosing the parameters of the support vector regression machine (SVR), the combination accuracy is proposed to evaluate the estimated effect. The methods of circulation crisscross verification and variable step length are used to search the optimal parameters. The interaction of the parameters is considered. This paper researches the influence of the combined form of parameters on the estimated accuracy, and assures the optimized combined form of the parameters. The result indicates the optimized combined form of the parameters can improve the expenses estimated accuracy.

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