LMI conditions for quadratic and robust D-stability of interval systems
基于启发式遗传算法的SVM模型自动选择

Motivated by the facts that automatic model selection for support vector machine(SVM) is an important issue to make it practically useful,and the commonly-used leave-one-out(LOO) method is complex and time consuming,we proposed an effective strategy for automatic model selection for SVM with Gauss kernel by using a heuristic real-coded genetic algorithm(GA).Based on the extensive analysis of the effects of the hyper-parameters on the generalization performance and two estimates of SVM,the appropriate fitness function for GA operation is determined.Simulations are performed on both artificial data and real data to demonstrate the effectiveness and efficiency of the proposed approach.The significance of the proposed method is its easy implementation and better performances in comparison with the commonly used loo method.