DocumentCode
441936
Title
Learning dynamic Bayesian network with immune evolutionary algorithm
Author
Jia, Hai-yang ; Liu, Da-you ; Yu, Peng
Author_Institution
Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun, China
Volume
5
fYear
2005
fDate
18-21 Aug. 2005
Firstpage
2934
Abstract
Dynamic Bayesian networks (DBNs) are directed graphical models of stochastic processes. How to learn the structure of DBNs from data is a hot problem of research. In this paper, the author presents an immune evolutionary algorithm for learning the network structure of DBNs. The results of contrast experiment prove that the constringency rate is more rapid than EGA-DBN algorithms.
Keywords
Bayes methods; belief networks; evolutionary computation; learning (artificial intelligence); stochastic processes; constringency rate; directed graphical model; dynamic Bayesian network; immune evolutionary algorithm; stochastic process; Artificial immune systems; Bayesian methods; Convergence; Evolutionary computation; Graphical models; Immune system; Machine learning; Machine learning algorithms; Probability distribution; Stochastic processes; Dynamic Bayesian Network; Immune evolutionary algorithm; Structural Learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location
Guangzhou, China
Print_ISBN
0-7803-9091-1
Type
conf
DOI
10.1109/ICMLC.2005.1527444
Filename
1527444
Link To Document