Research on Least Squares Support Vector Machine Combinatorial Optimization Algorithm

被引：4
|
作者：
Liu Taian [1 ,2 ]
Wang Yunjia [1 ]
Liu Wentong [3 ]
机构：
[1] CUMT, Coll Environm & Spatial Informat, Xuzhou 221116, Peoples R China
[2] SDUST, Dept Informat & Engn, Tai An 271019, Shandong, Peoples R China
[3] Nanyang Technol Univ, Singapore 639798, Singapore
来源：
2009 INTERNATIONAL FORUM ON COMPUTER SCIENCE-TECHNOLOGY AND APPLICATIONS, VOL 1, PROCEEDINGS | 2009年
基金：
中国国家自然科学基金;
关键词：
Least squares support vector machine; Sparse method; Combinatorial optimization algorithm; Linear equations least squares support vector machine;
D O I：
10.1109/IFCSTA.2009.116
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
LS-SVM(least squares support vector machine) has been widely used in engineering practice. However, the solving of LS-SVM still remains difficult under the condition of large sample. Based on algorithm of combinatorial optimization, this paper put forward the combinatorial optimization least squares support vector machine algorithm. On several different data aggregation of dimensions, the numerical value experiment and comparison are carried out on traditional LS-SVM algorithm, COLS-SVM algorithm and its improvement algorithm. The numerical value test has shown that COLS-SVM algorithm and its improvement algorithm are effective and have certain advantages on time and regression accuracy, compared with traditional LS-SVM algorithm.
引用
收藏
页码：452 / +
页数：2
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