Please use this identifier to cite or link to this item: https://olympias.lib.uoi.gr/jspui/handle/123456789/10749
Full metadata record
DC FieldValueLanguage
dc.contributor.authorVlassis, N.en
dc.contributor.authorLikas, A.en
dc.date.accessioned2015-11-24T17:00:20Z-
dc.date.available2015-11-24T17:00:20Z-
dc.identifier.issn1370-4621-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/10749-
dc.rightsDefault Licence-
dc.subjectem algorithmen
dc.subjectgaussian mixtureen
dc.subjectgreedy learningen
dc.subjectmaximum-likelihooden
dc.subjectmodelsen
dc.titleA greedy EM algorithm for Gaussian mixture learningen
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.publicationDate2002-
heal.abstractLearning a Gaussian mixture with a local algorithm like EM can be difficult because (i) the true number of mixing components is usually unknown, (ii) there is no generally accepted method for parameter initialization, and (iii) the algorithm can get trapped in one of the many local maxima of the likelihood function. In this paper we propose a greedy algorithm for learning a Gaussian mixture which tries to overcome these limitations. In particular, starting with a single component and adding components sequentially until a maximum number k, the algorithm is capable of achieving solutions superior to EM with k components in terms of the likelihood of a test set. The algorithm is based on recent theoretical results on incremental mixture density estimation, and uses a combination of global and local search each time a new component is added to the mixture.en
heal.journalNameNeural Processing Lettersen
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)

Files in This Item:
File Description SizeFormat 
Likas-2002-A greedy EM algorithm for Gaussian mixture learning.pdf123.34 kBAdobe PDFView/Open    Request a copy


This item is licensed under a Creative Commons License Creative Commons