Name
Abstract | ||
|---|---|---|
Dynamic Bayesian Network (DBN) is a graphical model for representing temporal stochastic processes. Learning the structure of DBN is a fundamental step for parameter learning, inference and application. For large scale problem, the structure learning is intractable. In some domains the training data is very limited and noisy, so learning the DBN structure only with training data is impractical. Domain knowledge may improve both the efficiency and the accuracy of the learning algorithm. But usually, the domain knowledge is uncertainty, unclear and even with conflict. This paper presents a novel algorithm for learning the structure of DBN, which consider the data and domain knowledge simultaneously, empirical experiment shows that the proposed algorithm improved the efficiency and the accuracy of the DBN structure learning. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICMLC.2012.6358942 | ICMLC |
Keywords | Field | DocType |
machine learning,belief networks,stochastic processes,inference mechanisms,learning (artificial intelligence),dynamic system,inference mechanism,dbn,graphical model,dynamic bayesian network,parameter learning,temporal stochastic processes,uncertainty handling,structure learning,bayesian network,domain knowledge,computational modeling,learning artificial intelligence | Stability (learning theory),Semi-supervised learning,Domain knowledge,Pattern recognition,Computer science,Inference,Wake-sleep algorithm,Bayesian network,Artificial intelligence,Graphical model,Machine learning,Dynamic Bayesian network | Conference |
Volume | ISSN | ISBN |
1 | 2160-133X | 978-1-4673-1484-8 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Chen | 1 | 109 | 30.89 |
| Haiyang Jia | 2 | 28 | 5.49 |
| Yuxiao Huang | 3 | 10 | 2.25 |
| Dayou Liu | 4 | 814 | 68.17 |