Analysis of detectors for support vector machines and least square support vector machines

被引：6
|
作者：
Kuh, A [1 ]
机构：
[1] Univ Hawaii, Dept Elect Engn, Honolulu, HI 96822 USA
来源：
PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3 | 2002年
关键词：
D O I：
10.1109/IJCNN.2002.1007643
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 discusses the performance capabilities of the Support Vector Machine (SVM) and the Least Squares (LS)-SVM for a two hypothesis detection problem. We consider a Bayesian framework where there are priors associated with each hypothesis and costs for making decisions. We examine how the SVM and the LS-SVM compare with the optimal Bayesian solution. We also discuss other merits for the SVM and the LS-SVM including practical implementation.
引用
收藏
页码：1075 / 1079
页数：3
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