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- W101175311 abstract "Learning of Bayesian networks aims at modeling thejoint density of a set of random variables from a ran-dom sample of joint observations of these variables(Nam et al., 2007). Such a graphical model maybe used for elucidating the conditional independencesholding in the datagenerating distribution, for auto-matic reasoning under uncertainties, and for Monte-Carlo simulations. Unfortunately, currently availablealgorithms for Bayesian network structure learningare either restrictive in the kind of distributions theysearch for, or of too high computational complexity tobe applicable in high dimensional spaces.Ensembles of weakly tted randomized models havebeen studied intensively and used successfully in thesupervised learning literature during the last twodecades. Among the advantages of these methods, letus quote the improved scalability of their learning al-gorithms thanks to randomization and the improvedpredictive accuracythe induced models thanks to theirhigher exibility in terms of bias/variance trade-o .For example, ensembles of extremely randomized treeshave been applied successfully in very complex high-dimensional tasks, such as image and sequence classi- cation (Geurts et al., 2006).In this work we explore the Perturb and Combineidea celebrated in supervised learning in the contextof probability density estimation in high-dimensionalspaces. We propose a new family of unsupervisedlearning methods of mixtures of large ensembles ofrandomly generated poly-trees. The speci c featureof these methods is their scalability to very large num-bers of variables and training instances. We explorevarious variants of these methods empirically on a setof discrete test problems of growing complexity." @default.
- W101175311 created "2016-06-24" @default.
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- W101175311 date "2008-05-19" @default.
- W101175311 modified "2023-09-26" @default.
- W101175311 title "Density estimation with ensembles of randomized poly-trees" @default.
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