mixture models; Markov trees; EM algorithm; bagging; Chow-Liu
Abstract :
[en] We study algorithms for learning Mixtures of Markov Trees for density estimation. There are two approaches to build such mixtures, which both exploit the interesting scaling properties of Markov Trees. We investigate whether the maximum likelihood and the variance reduction approaches can be combined together by building a two level Mixture of Markov Trees. Our experiments on synthetic data sets show that this two-level model outperforms the maximum likelihood one.
Research Center/Unit :
Systèmes et Modélisation
Disciplines :
Computer science
Author, co-author :
Schnitzler, François ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Wehenkel, Louis ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Two-level Mixtures of Markov Trees
Publication date :
29 June 2011
Number of pages :
A0
Event name :
The 11th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Event organizer :
Weiru Liu
Event place :
Belfast, Ireland
Event date :
from 29-05-2011-01-06-2011
Audience :
International
Funders :
FRIA - Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture Biomagnet IUAP network of the Belgian Science Policy Office Pascal2 network of excellence of the EC
