Possibilistic support vector machines

被引：15

作者：

Lee, K

Kim, DW

Lee, KH

Lee, D ^{[1
]}

机构：

[1] Korea Adv Inst Sci & Technol, Dept BioSyst & Adv Informat Technol Res Ctr, Taejon 305701, South Korea

[2] Korea Adv Inst Sci & Technol, Dept Elect Engn & Comp Sci, Taejon 305701, South Korea

来源：

PATTERN RECOGNITION | 2005年 / 38卷 / 08期

关键词：

classification; support vector machines; possibilistic SVMs; geometric distribution; possibilistic distance;

D O I：

10.1016/j.patcog.2004.11.018

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose new support vector machines (SVMs) that incorporate the geometric distribution of an input data set by associating each data point with a possibilistic membership, which measures the relative strength of the self class membership. By using a possibilistic distance measure based on the possibilistic membership, we reformulate conventional SVMs in three ways. The proposed methods are shown to have better classification performance than conventional SVMs in various tests. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

引用

页码：1325 / 1327

页数：3

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