Improving the Accuracy of Support Vector Machines.
Elnomery Allam Zanaty, Sultan Aljahdali · Computers and Their Applications · 2008
In this paper we introduce a new kernel function that could improve the SVMs classification accuracy. The proposed kernel function, called polynomial radial basis function (PRBF) combines both Gauss (RBF) and Polynomial (POLY) kernels. We prove that the proposed kernel converges faster than the Gauss and Polynomial kernels and also gives a good classification accuracy in nearly all the data sets specially with high dimension ones. Thereafter SVMs algorithm based on the PRBF is implemented and experimented with non-separable data set with several attributes to prove its efficiency. Then, the obtained results are compared with SVMs algorithms that are based on Gausian and Polynomial kernels.