標題: AN AUTOMATIC METHOD FOR SELECTING THE PARAMETER OF THE RBF KERNEL FUNCTION TO SUPPORT VECTOR MACHINES
作者: Li, Cheng-Hsuan
Lin, Chin-Teng
Kuo, Bor-Chen
Chu, Hui-Shan
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: Support vector machine;kernel method;optimal kernel
公開日期: 2010
摘要: 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 RBF 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 SVMs can obtain more accurate or at least equal performance than SVMs by applying k-fold cross-validation to determine the parameter.
URI: http://hdl.handle.net/11536/26310
http://dx.doi.org/10.1109/IGARSS.2010.5649251
ISBN: 978-1-4244-9566-5
DOI: 10.1109/IGARSS.2010.5649251
期刊: 2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
起始頁: 836
結束頁: 839
Appears in Collections:Conferences Paper


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