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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Denoeux, Thierry (Editor ), Masson, Marie-Hélène (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.
Edición:1st ed. 2012.
Colección:Advances in Intelligent and Soft Computing, 164
Temas:
Computational intelligence.
Artificial intelligence.
Computational Intelligence.
Artificial Intelligence.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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.

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