Communication Dans Un Congrès Année : 2008

High-dimensional probability density estimation with randomized ensembles of tree structured bayesian networks

Résumé

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 feature of these methods is their scalability to very large numbers of variables and training instances. We explore various simple variants of these methods empirically on a set of discrete test problems of growing complexity.

Fichier principal
Vignette du fichier
pgm-pt.pdf (349.28 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence
Loading...

Dates et versions

hal-00412288 , version 1 (17-04-2020)

Licence

Identifiants

  • HAL Id : hal-00412288 , version 1

Citer

Sourour Ammar, Philippe Leray, Boris Defourny, Louis Wehenkel. High-dimensional probability density estimation with randomized ensembles of tree structured bayesian networks. PGM 2008, 2008, Hirtshals, Denmark. pp.9-16. ⟨hal-00412288⟩
180 Consultations
456 Téléchargements

Partager

  • More