期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2014
卷号：5
期号：1
DOI：10.14569/IJACSA.2014.050103
出版社：Science and Information Society (SAI)
摘要：Support vector machines (SVMs) have been promising methods for classification and regression analysis because of their solid mathematical foundations which convey several salient properties that other methods hardly provide. However the performance of SVMs is very sensitive to how the kernel function is selected, the challenge is to choose the kernel function for accurate data classification. In this paper, we introduce a set of new kernel functions derived from the generalized Laguerre polynomials. The proposed kernels could improve the classification accuracy of SVMs for both linear and nonlinear data sets. The proposed kernel functions satisfy Mercer’s condition and orthogonally properties which are important and useful in some applications when the support vector number is needed as in feature selection. The performance of the generalized Laguerre kernels is evaluated in comparison with the existing kernels. It was found that the choice of the kernel function, and the values of the parameters for that kernel are critical for a given amount of data. The proposed kernels give good classification accuracy in nearly all the data sets, especially those of high dimensions.
