Name
Abstract | ||
|---|---|---|
In a lot of operational situations, we have to deal with uncertain and inaccurate information. The theory of belief functions is a mathematical framework useful to handle this kind of imperfection. However, in most of the cases, uncertain data are modeled with a distribution of probability. We present in this paper different principles to induce belief functions from probabilities. Hence, we decide to use these functions in a pattern recognition problem. We discuss about the results we obtain according the way we generate the belief function. To illustrate our work, it will be applied to seabed characterization. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICIF.2010.5711936 | Information Fusion |
Keywords | Field | DocType |
belief networks,pattern recognition,statistical distributions,belief function,pattern recognition,probability distribution,Continuous belief functions,decision making,least commitment,maximum of necessity,seabed characterization | Computer science,Uncertain data,Probability distribution,Artificial intelligence,Probability density function,Pattern recognition problem,Machine learning,Bayesian probability | Conference |
ISBN | Citations | PageRank |
978-0-9824438-1-1 | 1 | 0.41 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pierre-Emmanuel Doré | 1 | 1 | 0.41 |
| anthony fiche | 2 | 7 | 2.94 |
| Arnaud Martin | 3 | 158 | 18.26 |