Name
Abstract | ||
|---|---|---|
Bayesian Network parameter learning is one of the core issues of Bayesian Network research. The parameter estimation of Bayesian Network from large incomplete dataset can be very compute-intensive. A factor graph based Bayesian Network parameter learning algorithm using MapReduce is presented in this paper, which decomposes one Bayesian Network into factors and gets the Bayesian Network parameter through computing the conditional probability tables of each factor independently using Expectation Maximization (EM) algorithm within MapReduce framework. Experimental results show that when the number of training samples is 107, the speed of this parallel algorithm can get 2~6 times the speed of Sequential Expectation Maximization. The algorithm can reduce the training time significantly with increasing the number of Hadoop nodes. Compared with the existing parallel EM method using MapReduce, this algorithm has also a higher speed and can avoid the problem of load imbalance at the same time. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICTAI.2013.81 | ICTAI |
Keywords | Field | DocType |
belief networks,bayesian network parameter,expectation-maximisation algorithm,mapreduce,bayesian network research,factor graph,parameter estimation,learning (artificial intelligence),bayesian network parameter learning,bayesian network decomposition,expectation maximization,parameter learning,load imbalance,mapreduce framework,parallel algorithms,expectation maximization algorithm,hadoop nodes,bayesian network,data handling,parallel algorithm,graph theory,conditional probability tables,em algorithm,sequential expectation maximization,higher speed,probability,learning artificial intelligence | Factor graph,Variable-order Bayesian network,Conditional probability,Pattern recognition,Computer science,Parallel algorithm,Expectation–maximization algorithm,Wake-sleep algorithm,Bayesian network,Artificial intelligence,Machine learning,Dynamic Bayesian network | Conference |
ISSN | ISBN | Citations |
1082-3409 | 978-1-4799-2971-9 | 3 |
PageRank | References | Authors |
0.39 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Zhao | 1 | 186 | 33.54 |
| Jungang Xu | 2 | 30 | 11.88 |
| Yunjun Gao | 3 | 862 | 89.71 |