Research on compound learning algorithm of Bayesian networks structures

Xiao Liu; Haijun Li

doi:10.1117/12.717454

6 November 2006 Research on compound learning algorithm of Bayesian networks structures

Xiao Liu, Haijun Li

Author Affiliations +

Proceedings Volume 6357, Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence; 63574M (2006) https://doi.org/10.1117/12.717454
Event: Sixth International Symposium on Instrumentation and Control Technology, 2006, Beijing, China

Abstract

Today, there are more mature and relative perfect means of how to learn structures or parameters from completed data and learn parameters of fixed structure from uncompleted data. But it is a more difficult thing that learning structures of Bayesian Networks from uncompleted data. A compound learning algorithm is proposed; it combines the EM algorithm, Monte Carlo sampling algorithm and evolution algorithm together, uses EM algorithm to learn parameters of networks in uncompleted data, then samples the best network, converts the uncompleted data to completed data, and then evolves the structure using evolution algorithm. This algorithm could get over the defect of EM algorithm that frequently gains local maximum. Because data processing is based on posterior networks structures, structures of Bayesian Networks is optimizing and optimizing with evolution computing, the reliability of complementary data is higher. Learning rate is high and performance of this algorithm is good.

Citation Download Citation

Xiao Liu and Haijun Li "Research on compound learning algorithm of Bayesian networks structures", Proc. SPIE 6357, Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence, 63574M (6 November 2006); https://doi.org/10.1117/12.717454

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