The EQ Framework for Learning Equivalence Classes of Bayesian Networks

I
ICDM
2001
First IEEE International Conference on Data Mining (ICDM'01)
Abstract - The EQ Framework for Learning Equivalence Classes of Bayesian Networks

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	Similar Articles Articles by Paul Munteanu Articles by Mohamed Bendou

First IEEE International Conference on Data Mining (ICDM'01)

San Jose, California

November 29-December 02

ISBN: 0-7695-1119-8

	ASCII Text	x
	Paul Munteanu, Mohamed Bendou, "The EQ Framework for Learning Equivalence Classes of Bayesian Networks," Data Mining, IEEE International Conference on, pp. 417, First IEEE International Conference on Data Mining (ICDM'01), 2001.

	BibTex	x
	@article{ 10.1109/ICDM.2001.989547, author = {Paul Munteanu and Mohamed Bendou}, title = {The EQ Framework for Learning Equivalence Classes of Bayesian Networks}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2001}, isbn = {0-7695-1119-8}, pages = {417}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2001.989547}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - The EQ Framework for Learning Equivalence Classes of Bayesian Networks SN - 0-7695-1119-8 SP EP A1 - Paul Munteanu, A1 - Mohamed Bendou, PY - 2001 VL - 0 JA - Data Mining, IEEE International Conference on ER -

Paul Munteanu

Mohamed Bendou

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2001.989547

This paper proposes a theoretical and an algorithmic framework for the analysis and the design of efficient learning algorithms which explore the space of equivalence classes of Bayesian network structures.

This framework is composed of a generic learning model which uses essential graphs and more general partially directed graphs i order to represent the equivalence classes evaluated during search, operational characterizations of these graphs, processing procedures and formulas for directly calculating their score.

The experimental results of the algorithms designed within this framework show that the space of equivalence classes may be explored efficiently and with better results than the classical search in the space of Bayesian network structures.

Citation:

Paul Munteanu, Mohamed Bendou, "The EQ Framework for Learning Equivalence Classes of Bayesian Networks," icdm, pp.417, First IEEE International Conference on Data Mining (ICDM'01), 2001

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