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To explore the Perturb and Combine idea for estimating probability densities, we study mixtures of tree structured Markov networks derived by bagging combined with the Chow and Liu maximum weight spanning tree algorithm, or by pure random sampling. We empirically assess the performances of these methods in terms of accuracy, with respect to mixture models… (More)
In this work we explore the Perturb and Combine idea, celebrated in supervised learning, in the context of probability density estimation in high-dimensional spaces with graphical probabilistic models. We propose a new family of unsupervised learning methods of mixtures of large ensembles of randomly generated tree or poly-tree structures. The specific… (More)
The present work analyzes different randomized methods to learn Markov tree mixtures for density estimation in very high-dimensional discrete spaces (very large number n of discrete variables) when the sample size (N) is very small compared to n. Several sub-quadratic relaxations of the Chow-Liu algorithm are proposed, weakening its search procedure. We… (More)
The goal of this research is to improve probabilistic reasoning in high-dimensional problems.
2 Benelearn is the annual machine learning conference of Belgium and The Netherlands. It serves as a forum for researchers to exchange ideas, present recent work, and foster collaboration in the broad field of Machine Learning and its applications. The conference takes place in the Solcress seminar center, at walking distance from the center of the city of… (More)
The recent explosion of high dimensionality in datasets for several domains has posed a serious challenge to existing Bayesian network structure learning algorithms. Local search methods represent a solution in such spaces but suffer with small datasets. MMHC (Max-Min Hill-Climbing) is one of these local search algorithms where a first phase aims at… (More)