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College of Engineering(공과대학)Dept. of Bio and Brain Engineering(바이오및뇌공학과)BiS-Journal Papers(저널논문)

Possibilistic support vector machines

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dc.contributor.authorLee, Kko
dc.contributor.authorKim, DWko
dc.contributor.authorLee, Kwang-Hyungko
dc.contributor.authorLee, Doheonko
dc.date.accessioned2013-03-08T02:01:04Z-
dc.date.available2013-03-08T02:01:04Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued2005-08-
dc.identifier.citationPATTERN RECOGNITION, v.38, pp.1325 - 1327-
dc.identifier.issn0031-3203-
dc.identifier.urihttp://hdl.handle.net/10203/91785-
dc.description.abstractWe 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.-
dc.languageEnglish-
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD-
dc.titlePossibilistic support vector machines-
dc.typeArticle-
dc.identifier.wosid000229669900016-
dc.identifier.scopusid2-s2.0-18544388068-
dc.type.rimsART-
dc.citation.volume38-
dc.citation.beginningpage1325-
dc.citation.endingpage1327-
dc.citation.publicationnamePATTERN RECOGNITION-
dc.identifier.doi10.1016/j.patcog.2004.11.018-
dc.contributor.localauthorLee, Kwang-Hyung-
dc.contributor.localauthorLee, Doheon-
dc.contributor.nonIdAuthorLee, K-
dc.contributor.nonIdAuthorKim, DW-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorclassification-
dc.subject.keywordAuthorsupport vector machines-
dc.subject.keywordAuthorpossibilistic SVMs-
dc.subject.keywordAuthorgeometric distribution-
dc.subject.keywordAuthorpossibilistic distance-
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