On data based learning using support vector clustering

被引：0
|
作者：
Ribeiro, B [1 ]
机构：
[1] Univ Coimbra, Ctr Informat & Syst, Dept Informat Engn, P-3030 Coimbra, Portugal
来源：
ICONIP'02: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING: COMPUTATIONAL INTELLIGENCE FOR THE E-AGE | 2002年
关键词：
support vector machines (SVMs); kernel-based leaming algorithms; support vector clustering;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper addresses the effect of applying clustering algorithms, based on a distance metric rule, prior to support kernel learning in classification and regression problems. Self-Organising Maps (SOMs), which place emphasis in data domain description, and K-means clustering algorithms have been selected before applying a support vector algorithm which is based on a margin rule. Moreover, the recently developed support vector clustering algorithm, based on a cluster boundary rule, is applied in benchmark problems for comparison.
引用
收藏
页码：2516 / 2521
页数：6
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