PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

BNT Structure Learning Package : Documentation and Experiments
Philippe Leray and Olivier François
(2004) Technical Report. PSI Technical Reports, France.

Abstract

Bayesian networks are a formalism for probabilistic reasonning that is more and more used for classification task in data-mining. In some situations, the network structure is given by an expert, otherwise, retrieving it from a database is a NP-hard problem, notably because of the search space complexity. In the last decade, lot of methods have been introduced to learn the network structure automatically, by simplifying the search space (augmented naive bayes, K2) or by using an heuristic in this search space (greedy search). Most of these methods deal with completely observed data, but some others can deal with incomplete data (SEM, MWST-EM). The Bayes Net Toolbox introduced by [Murphy, 2001a] for Matlab allows us using Bayesian Networks or learning them. But this toolbox is not ’state of the art’ if we want to perform a Structural Learning, that’s why we propose this package.

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EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:343
Deposited By:Philippe Leray
Deposited On:15 December 2004