Name
Abstract | ||
|---|---|---|
Evidential networks are considered as a powerful and flexible tools, commonly used for analyzing complex systems and handling different types of uncertainty in data. A crucial step to benefit from the reasoning process in these models is to quantify them. Thus, we address, in this paper, the issue of estimating parameters in evidential networks from evidential databases, by applying the maximum likelihood estimation generalized to the evidence theory framework. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SOCPAR.2014.7008048 | SoCPaR |
Keywords | Field | DocType |
nickel,probabilistic logic,uncertainty,databases,cognition,maximum likelihood estimation | Complex system,Data mining,Pattern recognition,Computer science,Maximum likelihood,Artificial intelligence,Probabilistic logic,Estimation theory,Evidential reasoning approach,Database,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Narjes Ben Hariz | 1 | 0 | 0.68 |
| Boutheina Ben Yaghlane | 2 | 189 | 33.49 |