Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/574710
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dc.contributor.authorAMORIM, W. P.pt_BR
dc.contributor.authorPISTORI, H.pt_BR
dc.contributor.authorJACINTO, M. A. C.pt_BR
dc.contributor.otherWILLIAN PARAGUASSI AMORIM, UFMS/CAMPO GRANDE, MS; HEMERSON PISTORI, UCDB/CAMPO GRANDE, MS; MANUEL ANTONIO CHAGAS JACINTO, CPPSE.pt_BR
dc.date.accessioned2011-04-10T11:11:11Zpt_BR
dc.date.available2011-04-10T11:11:11Zpt_BR
dc.date.created2009-11-13pt_BR
dc.date.issued2009pt_BR
dc.identifier.other18778pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/574710pt_BR
dc.descriptionThis paper presents an attribute reduction comparative study on four linear discriminant analysis techniques: FisherFace, CLDA, DLDA and YLDA. The attribute reduction has been applied to the problem of leather defect c1assification using four different c1assifiers: C4.5, KNN, Naive Bayes and Support Veetor Machines. Results and analyses on the performance of correct c1assification rates as the number of attributes were reduced are reported.pt_BR
dc.description.uribitstream/CPPSE-2010/18778/1/PROCIMACJ2009.00194.pdfpt_BR
dc.format1 CD-ROMpt_BR
dc.languagept_BRpt_BR
dc.language.isoporpt_BR
dc.publisherIn: BRASILIAN SYMPOSIUM ON COMPUTER GRAPHICS AND IMAGE PROCESSING, 22., 2009, Rio de Janeiro. Proceedings... Rio de Janeiro: PUC, 2009.pt_BR
dc.relation.ispartofEmbrapa Pecuária Sudeste - Artigo em anais de congresso (ALICE)pt_BR
dc.subjectWet-bluept_BR
dc.subjectDefeito.pt_BR
dc.titleA comparative analysis of attribute reduction algorithms applied to wet-blue leather defects classification.pt_BR
dc.typeArtigo em anais de congresso (ALICE)pt_BR
dc.date.updated2016-05-09T11:11:11Zpt_BR
dc.subject.thesagroCouro.pt_BR
dc.description.notesDisponível em: http://www.matmidia.mat.puc-rio.br/sibgrapi2009/media/posters/59602.pdf. Acesso em 13 de novembro de 2009.pt_BR
dc.ainfo.id574710pt_BR
dc.ainfo.lastupdate2016-05-09pt_BR
Appears in Collections:Artigo em anais de congresso (CPPSE)

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