A SVM Method Trained by Improved Particle Swarm Optimization for Image Classification

摘要：

　　As an important classification method,SVM has been widely used in different fields.But it is still a problem how to choose the favorable parameters of SVM.For optimizing the parameters and increasing the accuracy of SVM,this paper proposed an improved quantum behaved particle swarm algorithm based on a mutation operator (MQPSO).The new operator is used for enhancing the global search ability of particle.We test SVM based on MPSO method on solving the problem of image classification.Result shows our algorithm is quite stable and gets higher accuracy.

关键词： PSO SVM Global search ability Parameter optimization Image classification

作者: Qifeng Qian Hao Gao Baoyun Wang

作者单位: College of Automation,Nanjing University of Posts and Telecommunications,Jiang Su 210023,China

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 263-272

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）