An automatic method for selecting the parameter of the RBF kernel function to support vector machines
An automatic method for selecting the parameter of the RBF kernel function to support vector machines
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DOI：
10.1109/igarss.2010.5649251
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发表时间：
2010-07
期刊：
影响因子：
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通讯作者：
Cheng-Hsaun Li;Chin-Teng Lin;Bor-Chen Kuo;Hui-Shan Chu
中科院分区：
文献类型：
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作者：
Cheng-Hsaun Li;Chin-Teng Lin;Bor-Chen Kuo;Hui-Shan Chu
支持向量机（SVM）是监督分类最强大的技术之一。然而，SVM 的性能取决于选择适当的核函数或核函数的适当参数。应用k折交叉验证（CV）来选择几乎最佳的参数是非常耗时的。但网格法的搜索范围和细度应事先确定。本文提出了一种自动选择RBF核函数参数的方法。在实验结果中，通过我们提出的方法选择参数比k倍交叉验证花费的时间非常少。此外，通过应用k折交叉验证来确定参数，相应的SVM可以获得比SVM更准确或至少相同的性能。
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.