Research on Least Squares Support Vector Machine Combinatorial Optimization Algorithm

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 Us improvement algorithm are effective and have certain advantages on time and regression accuracy, compared with traditional LS-SVM algorithm.

关键词： Least squares support vector machine Sparse method Combinatorial optimization algorithm Linear equations least squares support vector machine

作者: Liu Taian Wang Yunjia Liu Wentong

作者单位: College of Environment and Spatial Informatics, China University of Mining & Technology (CUMT),Xuzho College of Environment and Spatial Informatics, China University of Mining & Technology (CUMT),Xuzho Nanyang Technological University (NTU), Singapore, 639798, Singapore

会议类型: 国际会议

会议名称: 2009 International Forum on Computer Science-Technology and Applications(2009年国际计算机科学技术与应用论坛 IFCSTA 2009)

会议地点: 重庆

会议语种:英文

页码: 452-454

在线出版日期: 2009-12-25（万方平台首次上网日期，不代表论文的发表时间）