posted on 2001-01-01, 00:00authored byGang LiGang Li, F Tong, Honghua Dai
This paper formulates the problem of learning Bayesian network structures from data as determining the structure that best approximates the probability distribution indicated by the data. A new metric, Penalized Mutual Information metric, is proposed, and a evolutionary algorithm is designed to search for the best structure among alternatives. The experimental results show that this approach is reliable and promising.
History
Pagination
615 - 616
Location
San Jose, California
Open access
Yes
Start date
2001-11-29
End date
2001-12-02
ISBN-13
9780769511191
ISBN-10
0769511198
Language
eng
Publication classification
E1 Full written paper - refereed; E Conference publication
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