JISE


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Journal of Information Science and Engineering, Vol. 28 No. 1, pp. 1-15


An Automatic Method for Selecting the Parameter of the Normalized Kernel Function to Support Vector Machines

CHENG-HSUAN LI1, HSIN-HUA HO2, YU-LUNG LIU3, CHIN-TENG LIN1, BOR-CHEN KUO+ AND JIN-SHIUH TAUR2
1Institute of Electrical Control Engineering 
National Chiao Tung University 
Hsinchu, 300 Taiwan 
2Department of Electrical Engineering 
National Chung Hsing University 
Taichung, 402 Taiwan 
3Department of Computer Science and Information Engineering 
Asia University 
Taichung, 413 Taiwan 
+Graduate Institute of Educational Measurement and Statistics 
National Taichung University of Education 
Taichung, 403 Taiwan

    Soft-margin support vector machine (SVM) is one of the most powerful techniques for supervised classification. However, the performances of SVMs are based on choosing the proper kernel functions or proper parameters of a kernel function. It is extremely time consuming by applying the k-fold cross-validation (CV) to choose the almost best parameter. Nevertheless, the searching range and fineness of the grid method should be determined in advance. In this paper, an automatic method for selecting the parameter of the normalized kernel function is proposed. In the experimental results, it costs very little time than k-fold cross-validation for selecting the parameter by our proposed method. Moreover, the corresponding soft-margin SVMs can obtain more accurate or at least equal performance than the soft-margin SVMs by applying k-fold cross-validation to determine the parameters.

Keywords: soft-margin support vector machine, SVM, kernel method, optimal kernel, normalized kernel, k-fold cross-validation

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