Design of SVM based on radial basis function neural networks pre-partition

In order to solve the training time problem of the support vector machine for a large dataset, in this paper, an alternative approach motivated by the radial basis function neural network is developed to partition the subset of SVs for the SVM. The proposed method aims at obtain an optimal decision boundary based on the RBFNN, because it has good convergence and fast training. On the other hand, the method concerns on extracting a candidate set of the SVs from a large dataset using the decision boundary. The data structure of the RBFNN and SVM with Gaussian kernels is consistent in high dimensional feature space, moreover, the RBFNN was used to approximate any continuous nonlinear function, therefore, the subset can model the characteristics of the support vectors for a large dataset, and it is worth noting that the size of the subset is far smaller than the original training set. Experimental results show that the proposed method improves the performance of the SVM.