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

页码：1075 / 1079

页数：3

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