Stepwise Structure Learning Using Probabilistic Pruning for Bayesian Networks: Improving Efficiency and Comparing Characteristics View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Godai Azuma , Daisuke Kitakoshi , Masato Suzuki

ABSTRACT

This paper evaluates a structure learning method for Bayesian networks called Stepwise Structure Learning with Probabilistic pruning (SSL-Pro). Probabilistic pruning allows this method to obtain appropriate network structures while reducing computational time for structure learning. Computer experiments were conducted to investigate the characteristics of the SSL-Pro. Results showed that the SSL-Pro generally provided favorable performance, and revealed several parameter-setting guidelines to ensure reasonable learning. More... »

PAGES

533-543

Book

TITLE

Information Science and Applications 2017

ISBN

978-981-10-4153-2
978-981-10-4154-9

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-981-10-4154-9_62

DOI

http://dx.doi.org/10.1007/978-981-10-4154-9_62

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1084722463


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