Parameter learning for Bayesian networks with large data set
面向大规模数据集的贝叶斯网络参数学习算法

The creation of Bayesian networks can be separated into two tasks,structure learning and parameter learning.Expectation Maximization(EM) algorithm is a general method for parameter learning to incomplete data.The traditional EM algorithm has some shortcomings: it can't deal with large data sets,its convergence is slow and it easily results in local maximum.To overcome these shortcomings, large data set was divided into several small blocks and optimized in the small ones.Experiment results indicate that the improved EM algorithm has more advantages than standard EM.