SA Optimizing Algorithm of SVM Super-Parameters

Based on the statistical learning theory support vector machine focuses on the machine learning strategies under small samples and gets better generalization ability than those tools based on the experience risk minimization principle. Its classing or regression performance will be affected by relative super-parameters. An improved multi object optimization algorithm based on simulated annealing is proposed and applied to super-parameters optimization of the support vector machine with a RBF kernel. Then selection of proper searching space, initial feasible solution,initial temperature and design of an optimal object function are discussed in detail. The validation on the some standard datasets is carried out and its feasibility and effectiveness are confirmed.