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自动化与信息科学期刊
SJR: 0.232 SNIP: 0.464 CiteScore™: 0.27

ISSN 打印: 1064-2315
ISSN 在线: 2163-9337

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

自动化与信息科学期刊

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

ABSTRACT

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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