A Parallel and Incremental Approach for Data-Intensive Learning of Bayesian Networks

Bayesian network (BN) has been adopted as the underlying model for representing and inferring uncertain knowledge. As the basis of realistic applications centered on probabilistic inferences, learning a BN from data is a critical subject of machine learning, artificial intelligence, and big data par...

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Vydané v:	IEEE transactions on cybernetics Ročník 45; číslo 12; s. 2890 - 2904
Hlavní autori:	Yue, Kun, Fang, Qiyu, Wang, Xiaoling, Li, Jin, Liu, Weiyi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.12.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithm design and analysis Algorithms Bayesian analysis Bayesian network learning Cloud computing Computation Computational modeling Data models data-intensive computing Distributed databases Dynamic characteristics Heuristic algorithms incremental learning Learning MapReduce Mathematical models parallel algorithm Parallel algorithms Probability Scoring uncertain knowledge uncertain knowledge Bayesian network learning data-intensive computing incremental learning parallel algorithm MapReduce
ISSN:	2168-2267, 2168-2275, 2168-2275
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