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dc.contributor.authorAn, Jiyuan
dc.contributor.authorChen, Yi-Ping Phoebe
dc.contributor.editorHiroyuki Tarumi
dc.date.accessioned2017-05-03T16:58:53Z
dc.date.available2017-05-03T16:58:53Z
dc.date.issued2005
dc.date.modified2010-10-12T06:55:13Z
dc.identifier.doi10.1109/AMT.2005.1505422
dc.identifier.urihttp://hdl.handle.net/10072/25631
dc.description.abstractText categorization (TC) is one of the main applications of machine learning. Many methods have been proposed, such as Rocchio method, Naive bayes based method, and SVM based text classification method. These methods learn labeled text documents and then construct a classifier. A new coming text document's category can be predicted. However, these methods do not give the description of each category. In the machine learning field, there are many concept learning algorithms, such as, ID3 and CN2. This paper proposes a more robust algorithm to induce concepts from training examples, which is based on enumeration of all possible keywords combinations. Experimental results show that the rules produced by our approach have more precision and simplicity than that of other methods.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent1030409 bytes
dc.format.extent27833 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.languageEnglish
dc.language.isoeng
dc.publisherIEEE
dc.publisher.placePiscataway, USA
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencename2005 International Conference on Active Media Technology, 2005. (AMT 2005).
dc.relation.ispartofconferencetitleProceedings of the 2005 International Conference on Active Media Technology, 2005. (AMT 2005).
dc.relation.ispartofdatefrom2005-05-19
dc.relation.ispartofdateto2005-05-21
dc.relation.ispartoflocationTakamatsu, Japan
dc.rights.retentionY
dc.subject.fieldofresearchcode280213
dc.titleKeyword extraction for text categorization
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.rights.copyright© 2005 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.issued2005
gro.hasfulltextFull Text
gro.griffith.authorAn, Jay


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  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

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