Optimization of combined kernel function for SVM based on large margin learning theory

Kernel function plays a very important role in the performance of SVM. In order to improve generalization capability of SVM classifier, this paper proposes a new mechanism to optimize the parameters of combined kernel function by using large margin learning theory and a genetic algorithm, which aims to search the optimal parameters for the combined kernel function. This approach leads SVM to attain the maximum margin in the training dataset. The combined kernel function and the parameters obtained by the proposed approach leads to a better performance and results in a better SVM classifier. Both numerical simulation results and theoretical analysis show the effectiveness and feasibility of the proposed approach.