File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Applying the conjugate gradient method for text document categorization

TitleApplying the conjugate gradient method for text document categorization
Authors
KeywordsConjugate Gradient Method
Document Classification
Linear Least Squares Fit
Performance Measures
Issue Date2004
PublisherIEEE, Computer Society.
Citation
Proceedings - International Conference On Pattern Recognition, 2004, v. 2, p. 558-561 How to Cite?
AbstractIn this paper, we investigate the effectiveness of two different methods to solve the linear least squares fit (LLSF) problem for document categorization. The first method is the Singular Value Decomposition (SVD) method that has been previously used to solve the document categorization problem. The second method is the Conjugate Gradient (CG) method that is one of the most effective algorithms for solving a linear equation problem. However, up to our knowledge, the CG method has never been applied to handle the document classification, problem. Therefore, we compare the effectiveness of these two LLSF methods to categorize text documents. In addition, we examine the effect of using different term weighting schemes on their performance for document classification. Lastly, we compare the performance of the LLSF classifiers against the neighborhood-based Dt-kNN classifier, our best variant of the kNN classifier integrated with a dynamic threshold scheme, on the Reuters 21578 dataset. Besides being the first proposal to use the CG method for document classification, our work opens up many exciting directions for future investigation.
Persistent Identifierhttp://hdl.handle.net/10722/45793
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorTam, Ven_HK
dc.contributor.authorSetiono, Ren_HK
dc.contributor.authorSantoso, Aen_HK
dc.date.accessioned2007-10-30T06:35:36Z-
dc.date.available2007-10-30T06:35:36Z-
dc.date.issued2004en_HK
dc.identifier.citationProceedings - International Conference On Pattern Recognition, 2004, v. 2, p. 558-561en_HK
dc.identifier.issn1051-4651en_HK
dc.identifier.urihttp://hdl.handle.net/10722/45793-
dc.description.abstractIn this paper, we investigate the effectiveness of two different methods to solve the linear least squares fit (LLSF) problem for document categorization. The first method is the Singular Value Decomposition (SVD) method that has been previously used to solve the document categorization problem. The second method is the Conjugate Gradient (CG) method that is one of the most effective algorithms for solving a linear equation problem. However, up to our knowledge, the CG method has never been applied to handle the document classification, problem. Therefore, we compare the effectiveness of these two LLSF methods to categorize text documents. In addition, we examine the effect of using different term weighting schemes on their performance for document classification. Lastly, we compare the performance of the LLSF classifiers against the neighborhood-based Dt-kNN classifier, our best variant of the kNN classifier integrated with a dynamic threshold scheme, on the Reuters 21578 dataset. Besides being the first proposal to use the CG method for document classification, our work opens up many exciting directions for future investigation.en_HK
dc.format.extent978065 bytes-
dc.format.extent883769 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/pdf-
dc.languageengen_HK
dc.publisherIEEE, Computer Society.en_HK
dc.relation.ispartofProceedings - International Conference on Pattern Recognitionen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©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.en_HK
dc.subjectConjugate Gradient Methoden_HK
dc.subjectDocument Classificationen_HK
dc.subjectLinear Least Squares Fiten_HK
dc.subjectPerformance Measuresen_HK
dc.titleApplying the conjugate gradient method for text document categorizationen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1051-4651&volume=2&spage=558&epage=561&date=2004&atitle=Applying+the+conjugate+gradient+method+for+text+document+categorizationen_HK
dc.identifier.emailTam, V:vtam@eee.hku.hken_HK
dc.identifier.authorityTam, V=rp00173en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICPR.2004.1334305en_HK
dc.identifier.scopuseid_2-s2.0-10044290499en_HK
dc.identifier.hkuros103187-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-10044290499&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume2en_HK
dc.identifier.spage558en_HK
dc.identifier.epage561en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridTam, V=7005091988en_HK
dc.identifier.scopusauthoridSetiono, R=7005033162en_HK
dc.identifier.scopusauthoridSantoso, A=6601931777en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats