Accurate support vector machines for data classification
Elnomery Allam Zanaty, Sultan Aljahdali, Robert J. Cripps · International Journal of Rapid Manufacturing · 2009
In this paper, a new kernel function is introduced that improves the classification accuracy of support vector machines (SVMs) for both linear and non-linear data sets. The proposed kernel function, called Gauss radial basis polynomial function (RBPF) combine both Gauss radial basis function (RBF) and polynomial (POLY) kernels. It is shown that the proposed kernel converges faster than the RBF and POLY kernels. The accuracy of the proposed algorithm is compared to algorithms based on both Gaussian and polynomial kernels by application to a variety of non-separable data sets with several attributes. It is shown that the proposed kernel gives good classification accuracy in nearly all the data sets, especially those of high dimensions.