Hybrid optimization method for parameter selection of support vector machine

Parameter selection of support vector machine (SVM) is a key problem in the application of SVM, which directly has influence on generalization performance of SVM. By using the support vector bound which is modified by F measure as criterion function and using genetic simulated annealing algorithm to select kernel parameters and penalty factor, a method of parameter selection of SVM based on hybrid optimization strategy is proposed in this paper. The proposed method adequately combines the optimization advantages of genetic algorithm and simulated annealing algorithms. The experiment results demonstrate that, compared with cross validation method, this proposed method improves accuracy of SVM parameter selection and generalization performance of SVM.