Estimation de densité par ensembles aléatoires de poly-arbres

Ensembles of weakly fitted randomized models have been studied intensively and used successfully in the supervised learning literature during the last two decades. Among the advan- tages of these methods, let us quote the improved scalability of the learning algorithm thanks to its randomization and the improved predictive accuracy the induced models thanks to the higher flexibility in terms of bias/variance trade-off. In the present work we propose to explore this idea in the context of density estimation. We propose a new family of unsupervised learning methods of mixtures of large ensembles of ran-

[1]  Philippe Leray,et al.  Étude Comparative d’Algorithmes d’Apprentissage de Structure dans les Réseaux Bayésiens , 2004 .

[2]  Steffen L. Lauritzen,et al.  Bayesian updating in causal probabilistic networks by local computations , 1990 .

[3]  Philippe Leray,et al.  Réseaux bayésiens : Apprentissage et diagnostic de systemes complexes , 2006 .

[4]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.

[5]  Sanjoy Dasgupta,et al.  Learning Polytrees , 1999, UAI.

[6]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[7]  Judea Pearl,et al.  Fusion, Propagation, and Structuring in Belief Networks , 1986, Artif. Intell..

[8]  Nir Friedman,et al.  Being Bayesian about Network Structure , 2000, UAI.

[9]  D. Madigan,et al.  Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window , 1994 .

[10]  Marina Meila-Predoviciu,et al.  Learning with Mixtures of Trees , 1999 .

[11]  R. W. Robinson.  Counting unlabeled acyclic digraphs , 1977 .

[12]  Saharon Rosset,et al.  Boosting Density Estimation , 2002, NIPS.

[13]  J. York,et al.  Bayesian Graphical Models for Discrete Data , 1995 .

[14]  Adolfo J. Quiroz,et al.  Fast random generation of binary, t-ary and other types of trees , 1989 .

[15]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[16]  C. N. Liu,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[17]  Vladimir Pavlovic,et al.  Boosting and structure learning in dynamic Bayesian networks for audio-visual speaker detection , 2002, Object recognition supported by user interaction for service robots.

[18]  David Maxwell Chickering,et al.  Efficient Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables , 1997, Machine Learning.