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Authors: Mohamed BENDOU and Paul MUNTEANU

Affiliation: ESIEA Recherche, France

Keyword(s): Bayesian networks, equivalence classes, learning, largest chain graphs, essential graphs, instanciable partially oriented graph

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Bayesian Networks ; Enterprise Information Systems ; Soft Computing

Abstract: This paper proposes a new approach for designing learning bayesian network algorithms that explore the structure equivalence classes space. Its main originality consists in the representation of equivalence classes by largest chain graphs, instead of essential graphs which are generally used in the similar task. We show that this approach drastically simplifies the algorithms formulation and has some beneficial aspects on their execution time.

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Paper citation in several formats:
BENDOU, M. and MUNTEANU, P. (2004). LEARNING BAYESIAN NETWORKS WITH LARGEST CHAIN GRAPHS. In Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 972-8865-00-7; ISSN 2184-4992, SciTePress, pages 184-190. DOI: 10.5220/0002636301840190

@conference{iceis04,
author={Mohamed BENDOU. and Paul MUNTEANU.},
title={LEARNING BAYESIAN NETWORKS WITH LARGEST CHAIN GRAPHS},
booktitle={Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2004},
pages={184-190},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002636301840190},
isbn={972-8865-00-7},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - LEARNING BAYESIAN NETWORKS WITH LARGEST CHAIN GRAPHS
SN - 972-8865-00-7
IS - 2184-4992
AU - BENDOU, M.
AU - MUNTEANU, P.
PY - 2004
SP - 184
EP - 190
DO - 10.5220/0002636301840190
PB - SciTePress