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  5. Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)
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Please use this identifier to cite or link to this item: https://olympias.lib.uoi.gr/jspui/handle/123456789/10776
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dc.contributor.authorTitsias, M. K.en
dc.contributor.authorLikas, A.en
dc.date.accessioned2015-11-24T17:00:32Z-
dc.date.available2015-11-24T17:00:32Z-
dc.identifier.issn0162-8828-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/10776-
dc.rightsDefault Licence-
dc.subjectmixture modelsen
dc.subjectclassificationen
dc.subjectdensity estimationen
dc.subjectem algorithmen
dc.subjectcomponent sharingen
dc.subjectem algorithmen
dc.subjectmaximum-likelihooden
dc.titleClass conditional density estimation using mixtures with constrained component sharingen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
heal.publicationDate2003-
heal.abstractWe propose a generative mixture model classifier that allows for the class conditional densities to be represented by mixtures having certain subsets of their components shared or common among classes. We argue that, when the total number of mixture components is kept fixed, the most efficient classification model is obtained by appropriately determining the sharing of components among class conditional densities. In order to discover such an efficient model, a training method is derived based on the EM algorithm that automatically adjusts component sharing. We provide experimental results with good classification performance.en
heal.journalNameIeee Transactions on Pattern Analysis and Machine Intelligenceen
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
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