Learning dynamic Bayesian network with immune evolutionary algorithm

被引：0

作者：

Jia, HY ^{[1
]}

Liu, DY ^{[1
]}

Yu, P ^{[1
]}

机构：

[1] Jilin Univ, Sch Comp Sci & Technol, Key Lab Symbol Comp & Knowledge Engn, Minist Educ, Changchun 130023, Peoples R China

来源：

PROCEEDINGS OF 2005 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-9 | 2005年

关键词：

dynamic Bayesian network; structural learning; immune evolutionary algorithm;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：2934 / 2938

页数：5

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