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Journal of Information Science and Engineering, Vol. 28 No. 1, pp. 1-15 (January 2012)

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

Full Text () Retrieve PDF document (201201_01.pdf)

Received January 24, 2011; revised August 18, 2011; accepted August 30, 2011.
Communicated by Irwin King.
* The study was supported and funded by the National Science Council of Taiwan under Grants No. NSC 100- 2628-E-142-001-MY3 and 99-2221-E-142-002.
+ Corresponding author.