© 2014

Belief Functions: Theory and Applications

Third International Conference, BELIEF 2014, Oxford, UK, September 26-28, 2014. Proceedings

  • Fabio Cuzzolin
Conference proceedings BELIEF 2014

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8764)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 8764)

Table of contents

  1. Front Matter
  2. Belief Combination

    1. John Klein, Mehena Loudahi, Jean-Marc Vannobel, Olivier Colot
      Pages 1-10
    2. Frédéric Pichon, David Mercier, François Delmotte, Éric Lefèvre
      Pages 11-20
    3. Yanyan He, M. Yousuff Hussaini
      Pages 39-48
  3. Machine Learning

    1. Philippe Xu, Franck Davoine, Thierry Denoeux
      Pages 49-57
    2. Supanika Leurcharusmee, Peerapat Jatukannyaprateep, Songsak Sriboonchitta, Thierry Denoeux
      Pages 58-67
    3. Wiem Maalel, Kuang Zhou, Arnaud Martin, Zied Elouedi
      Pages 68-76
    4. Nicolas Sutton-Charani, Sébastien Destercke, Thierry Denœux
      Pages 87-94
    5. Malcolm J. Beynon
      Pages 95-104
  4. Applications 1

    1. Ahmed Samet, Éric Lefèvre, Sadok Ben Yahia
      Pages 105-114
    2. Salma Ben Dhaou, Mouloud Kharoune, Arnaud Martin, Boutheina Ben Yaghlane
      Pages 115-123
    3. Mira Bou Farah, David Mercier, François Delmotte, Éric Lefèvre, Sylvain Lagrue
      Pages 124-133
    4. Jean-Christophe Dubois, Yolande Le Gall, Arnaud Martin
      Pages 134-142
    5. Nopadon Kronprasert, Antti P. Talvitie
      Pages 143-152
  5. Theory 1

    1. Amel Ennaceur, Zied Elouedi, Éric Lefevre
      Pages 171-179

About these proceedings


This book constitutes the thoroughly refereed proceedings of the Third International Conference on Belief Functions, BELIEF 2014, held in Oxford, UK, in September 2014. The 47 revised full papers presented in this book were carefully selected and reviewed from 56 submissions. The papers are organized in topical sections on belief combination; machine learning; applications; theory; networks; information fusion; data association; and geometry.


Vehicular Ad-hoc Network (VANET) bayesian inference belief reasoning classification decision making document analysis evidential databases human factors object recognition on-destructive testing partially-supervised learning probabilistic database social networks statistical inference support vector machines target classification target tracking

Editors and affiliations

  • Fabio Cuzzolin
    • 1
  1. 1.Department of Computing and Communications TechnologiesOxford Brookes UniversityOxfordUK

Bibliographic information