Name
Abstract | ||
|---|---|---|
Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Bayesian networks are one of the most widely used class of these models. Some of the inference and learning tasks in Bayesian networks involve complex optimization problems that require the use of meta-heuristic algorithms. Evolutionary algorithms, as successful problem solvers, are promising candidates for this purpose. This paper reviews the application of evolutionary algorithms for solving some NP-hard optimization tasks in Bayesian network inference and learning. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.ins.2012.12.051 | Inf. Sci. |
Keywords | Field | DocType |
different problem domain,complex optimization problem,inference task,bayesian network,evolutionary algorithm,bayesian network inference,meta-heuristic algorithm,inherent property,intelligent system,successful problem solvers,np-hard optimization task,evolutionary computation | Intelligent control,Variable-order Bayesian network,Computer science,Inference,Wake-sleep algorithm,Bayesian network,Influence diagram,Artificial intelligence,Computational learning theory,Graphical model,Machine learning | Journal |
Volume | ISSN | Citations |
233, | 0020-0255 | 38 |
PageRank | References | Authors |
1.10 | 80 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pedro Larrañaga | 1 | 3882 | 208.54 |
| Hossein Karshenas | 2 | 147 | 7.79 |
| Concha Bielza | 3 | 909 | 72.11 |
| Roberto Santana | 4 | 357 | 19.04 |