Dimensionality reduction, classification and reconstruction problems in statistical learning approaches

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dc.contributor.authorGIRALDI, G. A.
dc.contributor.authorRODRIGUES, Paulo
dc.contributor.authorKITANI, E. C.
dc.contributor.authorTHOMAZ, C.
dc.date.accessioned2019-08-17T20:00:28Z
dc.date.available2019-08-17T20:00:28Z
dc.date.issued2008
dc.description.abstractalternativeStatistical learning theory explores ways of estimating functional dependency from a given collection of data. The specific sub-area of supervised statistical learning covers important models like Perceptron, Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). In this paper we review the theory of such models and compare their separating hypersurfaces for extracting group-differences between samples. Classification and reconstruction are the main goals of this comparison. We show recent advances in this topic of research illustrating their application on face and medical image databases.en
dc.description.firstpage141
dc.description.issuenumber1
dc.description.lastpage173
dc.description.volume15
dc.identifier.citationGIRALDI, G. A.; RODRIGUES, Paulo; KITANI, E. C.; THOMAZ, C. Dimensionality reduction, classification and reconstruction problems in statistical learning approaches. Revista de Informática Teórica e Aplicada (Impresso), v. 15, n.1, p. 141-173, 2008.
dc.identifier.doi10.22456/2175-2745.6016
dc.identifier.issn0103-4308
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/973
dc.identifier.urlhttps://doi.org/10.22456/2175-2745.6016
dc.relation.ispartofRevista de Informática Teórica e Aplicada (Impresso)
dc.rightsAcesso Aberto
dc.rights.license"Este é um artigo publicado em acesso aberto sob uma licença Creative Commons (CC BY-NC-ND 4.0 - ). Fonte: <https://seer.ufrgs.br/rita/about/editorialPolicies#openAccessPolicy>. Acesso em: 31/10/2019
dc.subject.otherlanguageImage analysisen
dc.subject.otherlanguageDimensionality reductionen
dc.titleDimensionality reduction, classification and reconstruction problems in statistical learning approachespt_BR
dc.typeArtigopt_BR
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