Denoeux / Masson Belief Functions: Theory and Applications

Proceedings of the 2nd International Conference on Belief Functions, Compiègne, France 9-11 May 2012
2012
ISBN: 978-3-642-29461-7
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 164, 444 Seiten, eBook

Reihe: Advances in Intelligent and Soft Computing

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 have been the starting points of many important developments, including the Transferable Belief Model and the Theory of Hints. The theory of belief functions is now well established as a general framework for reasoning with uncertainty, and has well understood connections to other frameworks such as probability, possibility and imprecise probability theories.  This volume contains the proceedings of the 2 nd International Conference on Belief Functions that was held in Compiègne, France on 9-11 May 2012. It gathers 51 contributions describing recent developments both on theoretical issues (including approximation methods, combination rules, continuous belief functions, graphical models and independence concepts) and applications in various areas including classification, image processing, statistics and intelligent vehicles.

Zielgruppe

Research

Autoren/Hrsg.

Denoeux, Thierry

Masson, Marie-Hélène

Inhaltsverzeichnis

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.