Reference : Online Learning of Gaussian Mixture Models - a Two-Level Approach

	Document type :	Scientific congresses and symposiums : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/2268/37204

Title :	Online Learning of Gaussian Mixture Models - a Two-Level Approach
Language :	English
Author, co-author :	Declercq, Arnaud [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Dép. d'électric., électron. et informat. (Inst.Montefiore) >]
	Piater, Justus [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > INTELSIG Group >]
Publication date :	2008
Main document title :	VISAPP 2008: Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1
Publisher :	INSTICC - Institute for Systems and Technologies of Information, Control and Communication
Pages :	605-611
On invitation :	No
Audience :	International
Event name :	International Conference on Computer Vision Theory and Applications (VISAPP)
Event date :	January 22-25, 2008
Event place (city) :	Funchal
Event country :	Madeira, Portugal
Keywords :	[en] Online learning ; Gaussian mixture model ; Uncertain model
Abstract :	[en] Online learning, Gaussian mixture model, Uncertain model. We present a method for incrementally learning mixture models that avoids the necessity to keep all data points around. It contains a single user-settable parameter that controls via a novel statistical criterion the trade-off between the number of mixture components and the accuracy of representing the data. A key idea is that each component of the (non-overfitting) mixture is in turn represented by an underlying mixture that represents the data very precisely (without regards to overfitting); this allows the model to be refined without sacrificing accuracy.
Funders :	Fonds pour la formation à la Recherche dans l'Industrie et dans l'Agriculture (Communauté française de Belgique) - FRIA
Permalink :	http://hdl.handle.net/2268/37204

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