Analysis of the Inter-class Distance-based Kernel Parameter Evaluating Method for RBF-SVM

doi:10.3969/j.issn.1000-1093.2012.02.012

Acta Armamentarii ›› 2012, Vol. 33 ›› Issue (2): 203-208.doi: 10.3969/j.issn.1000-1093.2012.02.012

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Analysis of the Inter-class Distance-based Kernel Parameter Evaluating Method for RBF-SVM

SONG Xiao-shan¹, JIANG Xiao-yu¹, LUO Jian-hua², YAO Jun¹

(1.Department of Control Engineering, Academy of Armored Force Engineering, Beijing 100072, China;2.Department of Science Research, Academy of Armored Force Engineering, Beijing 100072, China)

Received:2010-08-27 Revised:2010-08-27 Online:2014-03-04
Contact: SONG Xiao-shan E-mail:sxsh029@yahoo.com.cn

Abstract

Abstract: The effect of radial basis function (RBF) kernel parameter γ on the mapped space was analyzed. A novel kernel parameter evaluating method was proposed, which is based on the inter-class mean distance (ICMD). The theoretical and experimental analyses were made for the proposed method. Two sets of parameter selection experiments were made in order to prove the validity of the proposed method. In the first set, the optimal parameter γ was chosen by the ICMD method, and then the optimal cost parameter C was obtained by the 10-folds cross validation, which is called “Two-Stage Method”; In the second set, the 10-folds cross validation-based grid search method was adopted. The results show that the “Two-Stage Method” can select the optimal parameters with significantly decreased time cost, which proves the validity of ICMD method.

Key words: artificial intelligence, support vector machine, radial basis function kernel, kernel parameter evaluation, parameter selection

CLC Number:

TP181

SONG Xiao-shan, JIANG Xiao-yu, LUO Jian-hua, YAO Jun. Analysis of the Inter-class Distance-based Kernel Parameter Evaluating Method for RBF-SVM[J]. Acta Armamentarii, 2012, 33(2): 203-208.

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Analysis of the Inter-class Distance-based Kernel Parameter Evaluating Method for RBF-SVM

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