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

被引：0
|
作者：
Wang, Xiaodong [1 ,2 ,3 ]
Li, Mi [1 ,2 ,3 ]
Lu, Shengfu [1 ,2 ,3 ]
Zhong, Ning [1 ,2 ,3 ,4 ]
机构：
[1] Beijing Univ Technol, Int WIC Inst, Beijing, Peoples R China
[2] Beijing Int Collaborat Base Brain Informat & Wisd, Beijing, Peoples R China
[3] Beijing Key Lab MRI & Brain Informat, Beijing, Peoples R China
[4] Maebashi Inst Technol, Maebashi, Gunma, Japan
来源：
2015 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING APPLICATIONS (CSEA 2015) | 2015年
关键词：
Particle swarm optimization; support vector machines; parameters optimization; pattern recognition; FEATURE-SELECTION; SYSTEM;
D O I：
暂无
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
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.
引用
收藏
页码：665 / 669
页数：5
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