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dc.contributor.authorAn, Jiyuan
dc.contributor.authorChen, Yi-Ping Phoebe
dc.contributor.editorNing Zhong
dc.date.accessioned2017-05-03T16:58:53Z
dc.date.available2017-05-03T16:58:53Z
dc.date.issued2004
dc.date.modified2009-09-17T07:24:01Z
dc.identifier.doi10.1109/WI.2004.10069
dc.identifier.urihttp://hdl.handle.net/10072/25667
dc.description.abstractConcept learning of text documents can be viewed as the problem of acquiring the definition of a general category of documents. To definite the category of a text document, the Conjunctive of keywords is usually be used. These keywords should be fewer and comprehensible. A naﶥ method is enumerating all combinations of keywords to extract suitable ones. However, because of the enormous number of keyword combinations, it is impossible to extract the most relevant keywords to describe the categories of documents by enumerating all possible combinations of keywords. Many heuristic methods are proposed, such as GA-base, immune based algorithm. In this work, we introduce pruning power technique and propose a robust enumeration-based concept learning algorithm. Experimental results show that the rules produce by our approach has more comprehensible and simplicity than by other methods.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent151568 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherIEEE
dc.publisher.placeLos Alamitos, USA
dc.publisher.urihttp://ieeexplore.ieee.org/servlet/opac?punumber=9689
dc.relation.ispartofconferencenameWeb Intelligence, 2004. WI 2004.
dc.relation.ispartofconferencetitleProceedings: Web Intelligence, 2004. WI 2004
dc.relation.ispartofdatefrom2004-09-20
dc.relation.ispartofdateto2004-09-24
dc.relation.ispartoflocationBeijing
dc.subject.fieldofresearchcode280213
dc.titleConcept Learning of Text Documents
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.rights.copyright© 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
gro.date.issued2004
gro.hasfulltextFull Text
gro.griffith.authorAn, Jay


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