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dc.contributor.authorSharma, Aloken_US
dc.contributor.authorPaliwal, Kuldipen_US
dc.contributor.authorC. Onwubolu, Godfreyen_US
dc.date.accessioned2017-04-24T10:06:19Z
dc.date.available2017-04-24T10:06:19Z
dc.date.issued2005en_US
dc.identifier.issn15469239en_US
dc.identifier.doi10.3844/ajassp.2005.1445.1455en_US
dc.identifier.urihttp://hdl.handle.net/10072/4247
dc.description.abstractThis study firstly presents a survey on basic classifiers namely Minimum Distance Classifier (MDC), Vector Quantization (VQ), Principal Component Analysis (PCA), Nearest Neighbor (NN) and K-Nearest Neighbor (KNN). Then Vector Quantized Principal Component Analysis (VQPCA) which is generally used for representation purposes is considered for performing classification tasks. Some classifiers achieve high classification accuracy but their data storage requirement and processing time are severely expensive. On the other hand some methods for which storage and processing time are economical do not provide sufficient levels of classification accuracy. In both the cases the performance is poor. By considering the limitations involved in the classifiers we have developed Linear Combined Distance (LCD) classifier which is the combination of VQ and VQPCA techniques. The proposed technique is effective and outperforms all the other techniques in terms of getting high classification accuracy at very low data storage requirement and processing time. This would allow an object to be accurately classified as quickly as possible using very low data storage capacity.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherScience Publicationsen_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom1445en_US
dc.relation.ispartofpageto1455en_US
dc.relation.ispartofissue10en_US
dc.relation.ispartofjournalAmerican Journal of Applied Sciencesen_US
dc.relation.ispartofvolume2en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchcode280207en_US
dc.titlePattern classification: An improvement using combination of VQ and PCA techniquesen_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.issued2015-02-04T04:25:26Z
gro.hasfulltextNo Full Text


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