Least Squares Support Vector Machines Based on Support Vector Degrees

被引：1
|
作者：
Li, Lijuan [1 ]
Li, Youfeng [1 ]
Su, Hongye [1 ]
Chu, Jian [1 ]
机构：
[1] Zhejiang Univ, Natl Lab Ind Control Technol, Inst Adv Proc Control, Hangzhou 310027, Peoples R China
来源：
INTELLIGENT COMPUTING, PART I: INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING, ICIC 2006, PART I | 2006年 / 4113卷
关键词：
D O I：
10.1007/11816157_160
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A modified least squares support vector machines (LS-SVM) approach, which treats the training data points differently according to their different degrees of importance, is proposed in this paper. On each data point, a support vector degree is defined and it is associated with the corresponding absolute value of Lagrange multiplier. The experiment of identification of pH neutralization process with polluted measuring data is shown in this paper and the result indicates that the method is effective in identification of nonlinear system. By contrast with the basic LS-SVM, the result also shows the priority of the presented new algorithm.
引用
收藏
页码：1275 / 1281
页数：7
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