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Supervised and unsupervised classification using mixture models

hal.structure.identifierModelling and Inference of Complex and Structured Stochastic Systems [MISTIS]
dc.contributor.authorGIRARD, Stéphane
hal.structure.identifierQuality control and dynamic reliability [CQFD]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorSARACCO, Jerome
dc.date.accessioned2024-04-04T03:16:49Z
dc.date.available2024-04-04T03:16:49Z
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/194249
dc.description.abstractEnThis chapter is dedicated to model-based supervised and unsuper-vised classification. Probability distributions are defined over possible labels as well as over the observations given the labels. To this end, the basic tools are the mixture models. This methodology yields a posterior distribution over the labels given the observations which allows to quantify the uncertainty of the classification. The role of Gaussian mixture models is emphasized leading to Linear Discriminant Analysis and Quadratic Discriminant Analysis methods. Some links with Fisher Discriminant Analysis and logistic regression are also established. The Expectation-Maximization algorithm is introduced and compared to the K-means clustering method. The methods are illustrated both on simulated datasets as well as on real datasets using the R software.
dc.language.isoen
dc.title.enSupervised and unsupervised classification using mixture models
dc.typeDocument de travail - Pré-publication
dc.subject.halMathématiques [math]/Statistiques [math.ST]
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
hal.identifierhal-01241818
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01241818v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=GIRARD,%20St%C3%A9phane&SARACCO,%20Jerome&rft.genre=preprint


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