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

被引：0
|
作者：
Qian, Qifeng [1 ]
Gao, Hao [1 ]
Wang, Baoyun [1 ]
机构：
[1] Nanjing Univ Posts & Telecommun, Coll Automat, Nanjing 210023, Jiangsu, Peoples R China
来源：
PATTERN RECOGNITION (CCPR 2014), PT I | 2014年 / 483卷
关键词：
PSO; SVM; Global search ability; Parameter optimization; Image classification;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
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.
引用
收藏
页码：263 / 272
页数：10
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