Using support vector machines for mining regression classes in large data sets

被引：0
|
作者：
Sun, ZH [1 ]
Gao, LX [1 ]
Sun, YX [1 ]
机构：
[1] Zhejiang Univ, Natl Lab Ind Control Technol, Hangzhou 310027, Peoples R China
来源：
2002 IEEE REGION 10 CONFERENCE ON COMPUTERS, COMMUNICATIONS, CONTROL AND POWER ENGINEERING, VOLS I-III, PROCEEDINGS | 2002年
关键词：
support vector machines; regression classes; data mining;
D O I：
10.1109/TENCON.2002.1181221
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Support vector machines (SVMs) overcome the limit of the maximum-likelihood method that only applies to very limited set of the density functions. They call estimate simultaneous the regression classes in the mixture data set. The validity of the SVMs was demonstrated in the experiments. The results indicate that the SVMs can estimate the regression classes in the mixture data set with noise.
引用
收藏
页码：89 / 92
页数：4
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