Discovery and Fusion of Uncertain Knowledge in Data

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. Annotation ©2018 Ringgold, Inc., Portland, OR (protoview.com)