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Theses

De l'identification de structure de réseaux bayésiens à la reconnaissance de formes à partir d'informations complètes ou incomplètes.

Abstract : We have performed an empirical study of various deterministic Bayesian
networks structure learning algorithms.
The first test step has allowed us to emphasise which learning
technics need a specific initialisation et we have proposed a way to
do this.
In the second stage of this doctoral study, we have adapted some
learning technics to incomplete datasets.
Then, we have proposed an efficient algorithm to learn a
tree-augmented naive Bayes classifier in a general way from an
incomplete dataset.
We have also introduced an original formalism to model incomplete
dataset generation processes with MCAR or MAR assumptions.
Finally, various synthetic datasets and real datasets have been used
to empirically compare structure learning methods from incomplete
datasets.
Complete list of metadatas

https://tel.archives-ouvertes.fr/tel-00126033
Contributor : Olivier Francois <>
Submitted on : Tuesday, January 23, 2007 - 2:09:30 PM
Last modification on : Friday, October 23, 2020 - 4:38:13 PM
Long-term archiving on: : Friday, September 21, 2012 - 10:25:45 AM

Identifiers

  • HAL Id : tel-00126033, version 1

Citation

Olivier Francois. De l'identification de structure de réseaux bayésiens à la reconnaissance de formes à partir d'informations complètes ou incomplètes.. Modélisation et simulation. INSA de Rouen, 2006. Français. ⟨tel-00126033⟩

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