Application and Parameters Optimization of SVM Based on Adaptive Mutation Particle Swarm Optimization

摘要：

　　This study proposed an adaptive mutation particle swarm optimization (AMPSO) for parameters optimization of support vector machines (SVM).The improved inertia weight and mutation mechanism aimed to balance the global and local search, which could improve the recognition accuracy of SVM.The experimental results showed that compared with grid and particle swarm optimization (PSO), classification accuracy of the proposed AMPSO-SVM model can be significantly increased.

关键词： Particle swarm optimization support vector machines parameters optimization pattern recognition

作者: Xiaodong Wang Mi Li Shengfu Lu Ning Zhong

作者单位: International WIC Institute, Beijing University of Technology, Beijing, China;Beijing International International WIC Institute, Beijing University of Technology, Beijing, China;Beijing International

会议类型: 国际会议

会议名称: International Conference on Computational Science and Engineering Applications(CSEA2015)2015计算机科学与工程应用国际会议

会议地点: 三亚

会议语种:英文

页码: 665-669

在线出版日期: 2015-12-26（万方平台首次上网日期，不代表论文的发表时间）