Research on Combination Optimization of Parameters and Character Choice for SVM Based on Simulated Annealing and Improved QPSO

被引：0
|
作者：
Li Wanling [1 ]
Liu Yaozhou [1 ]
Deng Daquan [1 ]
机构：
[1] Ordnance Technol Res Inst Shijiazhuang, Shijiazhuang, Peoples R China
来源：
MECHATRONICS ENGINEERING, COMPUTING AND INFORMATION TECHNOLOGY | 2014年 / 556-562卷
关键词：
SVM; combination optimization; character choice; QPSO; IQPSO; Simulated Annealing;
D O I：
10.4028/www.scientific.net/AMM.556-562.3384
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In order to improve the classification accuracy of SVM, combination optimization of parameters and character choice for SVM was proposed. Improved QPSO algorithm was researched on in this paper. At the same time, simulated annealing and improved QPSO algorithm was adopted to train SVM in this paper in order to improve its training speed and diagnosis precision. At last the method mentioned above was applied to train and validate SVM using UCI data, and the result showed that this method was very good.
引用
收藏
页码：3384 / 3387
页数：4
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