Belief functions : theory and applications : proceedings of the 2nd International Conference on Belief Functions, Compiègne, France 9-11 May 2012
The theory of belief functions, also known as evidence theory or Dempster-Shafer theory, was first introduced by Arthur P. Dempster in the context of statistical inference, and was later developed by Glenn Shafer as a general framework for modeling epistemic uncertainty. These early contributions ha...
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| Outros Autores: | , |
|---|---|
| Formato: | Livre numérique |
| Idioma: | Anglais |
| Publicado em: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Colecção: | Advances in Intelligent and Soft Computing
164 |
| Acesso em linha: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Belief functions, theory and applications, proceedings of the 2nd International Conference on Belief Functions, Compiègne, France 9-11 May 2012, Thierry Denoeux and Marie-Hélène Masson (eds.), Berlin, Springer, 2012, 1 vol. (XI-442 p.), Advances in intelligent and soft computing, 978-3-642-29460-0 |
Sumário:
- From the content: On belief functions and random sets Evidential Multi-label classification method using the Random k-Label sets approach An Evidential Improvement for Gender Profiling An Interval-Valued Dissimilarity Measure for Belief Functions Based on Credal Semantics An evidential pattern matching approach for vehicle identification Comparison between a Bayesian approach and a method based on continuous belief functions for pattern recognition Prognostic by classification of predictions combining similarity-based estimation and belief functions Adaptative initialisation of a EvKNN classification algorithm