Motivated by the fact that automatic parameters selection for Support Vector Machine(SVM) is an important issue to make SVM practically useful and the common used Leave-One-Out(LOO) method is complex calculation and t...Motivated by the fact that automatic parameters selection for Support Vector Machine(SVM) is an important issue to make SVM practically useful and the common used Leave-One-Out(LOO) method is complex calculation and time consuming,an effective strategy for automatic parameters selection for SVM is proposed by using the Particle Swarm Optimization(PSO) in this paper.Simulation results of practice data model demonstrate the effectiveness and high efficiency of the proposed approach.展开更多
The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results...The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.展开更多
文摘Motivated by the fact that automatic parameters selection for Support Vector Machine(SVM) is an important issue to make SVM practically useful and the common used Leave-One-Out(LOO) method is complex calculation and time consuming,an effective strategy for automatic parameters selection for SVM is proposed by using the Particle Swarm Optimization(PSO) in this paper.Simulation results of practice data model demonstrate the effectiveness and high efficiency of the proposed approach.
基金the National Nature Science Foundation of China (60775047, 60402024)
文摘The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.
