DocumentCode :
1562977
Title :
A comparatively research in incremental learning of Bayesian networks
Author :
Huang, Hao ; Song, Hantao ; Tian, Fengzhan ; Lu, Yuchang ; Wang, Quande
Author_Institution :
Dept. of Comput. Sci. & Technol., Beijing Inst. of Technol., China
Volume :
5
fYear :
2004
Firstpage :
4260
Abstract :
According to the way that data is processed, the learning algorithms may be classified as batch or incremental method. It is an obvious need for improving the performance and accuracy of a Bayesian network as new data is observed. While incremental learning parameters for a fixed structure have been accomplished, incremental update of Bayesian network structure is still an open problem. We have investigated the three main algorithms in incremental learning of Bayesian networks and present our theoretical analysis results. We have pointed out the main differences among the three incremental learning algorithms. Then we present our experiment result to support our theoretical analysis.
Keywords :
belief networks; learning (artificial intelligence); Bayesian network structure; incremental learning; incremental method; learning algorithms; Algorithm design and analysis; Bayesian methods; Clustering algorithms; Computer science; Decision making; Information technology; Intelligent networks; Knowledge representation; Learning; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
Print_ISBN :
0-7803-8273-0
Type :
conf
DOI :
10.1109/WCICA.2004.1342314
Filename :
1342314
Link To Document :
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