DocumentCode :
1602839
Title :
A review of some Bayesian Belief Network structure learning algorithms
Author :
Mittal, Sangeeta ; Maskara, S.L.
Author_Institution :
Dept. of CS&IT, JIIT, Noida, India
fYear :
2011
Firstpage :
1
Lastpage :
5
Abstract :
Bayesian Belief Networks (BBNs) are useful in modeling complex situations. Such graphical models help in giving better insight and understanding of the situation. Many algorithms for machine learning of BBN structures have been developed. In this paper six different algorithms have been reviewed by constructing BBN structures for two different datasets using various algorithms. Some inferences have been drawn from the results obtained from the study which may help in decision making.
Keywords :
belief networks; inference mechanisms; learning (artificial intelligence); BBN structures; Bayesian belief network structure learning algorithms; decision making; graphical models; machine learning; Bayesian methods; Diseases; Inference algorithms; Learning systems; Machine learning algorithms; Size measurement; Bayesian Belief Network; Hepatitis Domain Diseases; Structure Learning; Urinary System Diseases;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information, Communications and Signal Processing (ICICS) 2011 8th International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4577-0029-3
Type :
conf
DOI :
10.1109/ICICS.2011.6173579
Filename :
6173579
Link To Document :
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