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Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
Journal of Automation and Information Sciences
SJR: 0.275 SNIP: 0.59 CiteScore™: 0.8

ISSN Печать: 1064-2315
ISSN Онлайн: 2163-9337

Выпуски:
Том 52, 2020 Том 51, 2019 Том 50, 2018 Том 49, 2017 Том 48, 2016 Том 47, 2015 Том 46, 2014 Том 45, 2013 Том 44, 2012 Том 43, 2011 Том 42, 2010 Том 41, 2009 Том 40, 2008 Том 39, 2007 Том 38, 2006 Том 37, 2005 Том 36, 2004 Том 35, 2003 Том 34, 2002 Том 33, 2001 Том 32, 2000 Том 31, 1999 Том 30, 1998 Том 29, 1997 Том 28, 1996

Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v43.i10.10
pages 1-9

Fast Algorithm for Learning the Bayesian Networks From Data

Alexander S. Balabanov
Institute of Software Systems of National Academy of Sciences of Ukraine, Kyiv, Ukraine
Alexander S. Gapyeyev
V.M. Glushkov Institute of Cybernetics of National Academy of Sciences of Ukraine, Kiev
Anatoliy M. Gupal
V.M. Glushkov Institute of Cybernetics of National Academy of Sciences of Ukraine, Kiev, Ukraine
Sergey S. Rzhepetskiy
V.M. Glushkov Institute of Cybernetics of National Academy of Sciences of Ukraine, Kiev

Краткое описание

The new constraint-based algorithm for learning dependency structures from data is developed. The novelty of the proposed algorithm is conditioned by the rules of acceleration of inductive inference, which drastically reduce the search area of separators while derivation of the model skeleton. On examples of the Bayesian networks of moderate saturation we have demonstrated that proposed algorithm learns Bayesian nets (of moderate density) multiple times faster than well-known PC algorithm.


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