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Discovery and Fusion of Uncertain Knowledge in Data.


Discovery and Fusion of Uncertain Knowledge in Data

Kun Yue, Weiyi Liu, Hao Wu, Dapeng Tao, and Ming Gao

World Scientific


202 pages



East China Normal University Scientific Reports; Volume 6


Adopting Bayesian network as the framework of knowledge representation and inferences, the Chinese authors explore new approaches to uncertain knowledge discovery and fusion by incorporating the massive, distributed, uncertain, and dynamically changing characteristics concerned in data analysis applications. The book proposes a parallel and incremental approach for data-intensive learning by extending the classic scoring and search algorithm and using MapReduce, and develops semantics-preserving methods for the fusion of logical and probabilistic knowledge and that of time-series probabilistic knowledge. ([umlaut] Ringgold, Inc., Portland, OR)

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Title Annotation:Kun Yue, Weiyi Liu, Hao Wu, Dapeng Tao, and Ming Gao
Article Type:Book review
Date:Jan 1, 2018
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