A survey on Bayesian network structure learning from data

A necessary step in the development of artificial intelligence is to enable a machine to represent how the world works, building an internal structure from data. This structure should hold a good trade-off between expressive power and querying efficiency. Bayesian networks have proven to be an effec...

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Veröffentlicht in:	Progress in artificial intelligence Jg. 8; H. 4; S. 425 - 439
Hauptverfasser:	Scanagatta, Mauro, Salmerón, Antonio, Stella, Fabio
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2019 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Bayesian analysis Bioinformatics Computational Intelligence Computer Imaging Computer Science Continuity (mathematics) Control Data Mining and Knowledge Discovery Dependence Economic analysis Image processing Machine learning Mechatronics Missing data Natural Language Processing (NLP) Pattern Recognition and Graphics Power efficiency Review Risk analysis Robotics Software Software development tools Vision Structure learning Statistics Bayesian network Machine learning
ISSN:	2192-6352, 2192-6360
Online-Zugang:	Volltext
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