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Cited 1 time in webofscience Cited 3 time in scopus
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dc.contributor.authorYoon, Y-
dc.contributor.authorLee, C-
dc.contributor.authorLee, GG-
dc.date.accessioned2016-04-01T02:12:25Z-
dc.date.available2016-04-01T02:12:25Z-
dc.date.created2009-03-18-
dc.date.issued2005-01-
dc.identifier.issn0302-9743-
dc.identifier.other2005-OAK-0000005028-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/24655-
dc.description.abstractIn a text categorization task, classification on some hierarchy of classes shows better results than the case without the hierarchy. In current environments where large amount of documents are divided into several subgroups with a hierarchy between them, it is more natural and appropriate to use a hierarchical classification method. We introduce a new internal node evaluation scheme which is very helpful to the development process of a hierarchical classifier. We also show that the hierarchical classifier construction method using this measure yields a classifier with better classification performance especially when applied to the classification task with large depth of hierarchy.-
dc.description.statementofresponsibilityX-
dc.languageEnglish-
dc.publisherSPRINGER-VERLAG BERLIN-
dc.relation.isPartOfLECTURE NOTES IN COMPUTER SCIENCE-
dc.titleSystematic construction of hierarchical classifier in SVM-based text categorization-
dc.typeArticle-
dc.contributor.college컴퓨터공학과-
dc.identifier.doi10.1007/978-3-540-30211-7_65-
dc.author.googleYoon, Y-
dc.author.googleLee, C-
dc.author.googleLee, GG-
dc.relation.volume3248-
dc.relation.startpage616-
dc.relation.lastpage625-
dc.contributor.id10103841-
dc.relation.journalLECTURE NOTES IN COMPUTER SCIENCE-
dc.relation.indexSCI급, SCOPUS 등재논문-
dc.relation.sciSCIE-
dc.collections.nameConference Papers-
dc.type.rimsART-
dc.identifier.bibliographicCitationLECTURE NOTES IN COMPUTER SCIENCE, v.3248, pp.616 - 625-
dc.identifier.wosid000228359800065-
dc.date.tcdate2018-03-23-
dc.citation.endPage625-
dc.citation.startPage616-
dc.citation.titleLECTURE NOTES IN COMPUTER SCIENCE-
dc.citation.volume3248-
dc.contributor.affiliatedAuthorLee, GG-
dc.identifier.scopusid2-s2.0-26444503656-
dc.description.journalClass1-
dc.description.journalClass1-
dc.type.docTypeArticle; Proceedings Paper-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-

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