期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2013
卷号：6
期号：5
页码：437-446
出版社：SERSC
摘要：Support vector machine (SVM) is a kind of machine learning method, but the selection of parameters has important effects on the generalization ability of SVMs. In this study, the relation between the error penalty parameter C, kernel parameter σ and the generalization ability of SVMs is discussed. Parameter C adjusts the similarity among within-class members, while parameter σ adjusts the similarity between classes. Moreover, C and σ balances each other mutually within a certain range, which forms a fan-shaped optional parameter distribution region. The optimal parameter area should be located near the center of the sector where both C and σ are small. According to this, a method is suggested to first search a suitable area with coarse grids, and then determine the optimal parameter within the area with a fine bilinear grid. Experimental results show that the new parameter selection method can not only avoid local optima, and thus excluding the cases in which C and σ are big and unstable, but also can be extremely fast in searching process. Compared with other parameter selection methods, the performance of SVMs cannot be influenced, or even better in some cases.
