Name
Abstract | ||
|---|---|---|
Evidential graphical models are considered as an efficient tool for representing and analyzing complex and real-world systems, and reasoning under uncertainty. This work raises the issue of estimating the different parameters of these networks. More precisely, we address the problem of updating these parameters when getting new data without repeating the learning process from the beginning. Indeed, we propose a new incremental approach to update the different parameters based on the combination rules proposed in the evidence framework. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-60045-1_19 | ADVANCES IN ARTIFICIAL INTELLIGENCE: FROM THEORY TO PRACTICE (IEA/AIE 2017), PT II |
Keywords | Field | DocType |
Parameter estimation,Graphical models,Belief function theory,Incremental learning,Evidential data | Computer science,Incremental learning,Belief function theory,Artificial intelligence,Graphical model,Estimation theory,Machine learning | Conference |
Volume | ISSN | Citations |
10351 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Narjes Ben Hariz | 1 | 0 | 0.68 |
| Boutheina Ben Yaghlane | 2 | 189 | 33.49 |