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dc.contributor.authorSharma, Aloken_US
dc.contributor.authorPaliwal, Kuldipen_US
dc.contributor.authorC. Onwubolu, Godfreyen_US
dc.date.accessioned2017-04-24T10:06:23Z
dc.date.available2017-04-24T10:06:23Z
dc.date.issued2006en_US
dc.date.modified2009-09-21T05:48:35Z
dc.identifier.issn00313203en_US
dc.identifier.doi10.1016/j.patcog.2006.02.001en_AU
dc.identifier.urihttp://hdl.handle.net/10072/14347
dc.description.abstractSeveral pattern classifiers give high classification accuracy but their storage requirements and processing time are severely expensive. On the other hand, some classifiers require very low storage requirement and processing time but their classification accuracy is not satisfactory. In either of the cases the performance of the classifier is poor. In this paper, we have presented a technique based on the combination of minimum distance classifier (MDC), class-dependent principal component analysis (PCA) and linear discriminant analysis (LDA) which gives improved performance as compared with other standard techniques when experimented on several machine learning corpuses.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherPergamonen_US
dc.publisher.placeUnited Kingdomen_US
dc.publisher.urihttp://www.elsevier.com/wps/find/journaldescription.cws_home/328/description#descriptionen_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom1215en_US
dc.relation.ispartofpageto1229en_US
dc.relation.ispartofjournalPattern Recognitionen_US
dc.relation.ispartofvolume39en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280207en_US
dc.titleClass-dependent PCA, MDC and LDA: A combined classifier for pattern classificationen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.date.issued2006
gro.hasfulltextNo Full Text


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