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Book chapters

  1. T. Denoeux, D. Dubois and H. Prade. Representations of Uncertainty in Artificial Intelligence: Probability and Possibility. In P. Marquis, O. Papini and H. Prade (Eds), “A Guided Tour of Artificial Intelligence Research”, Volume 1, Chapter 3, Springer Verlag, pages 69-117, 2020. pdf
  2. T. Denoeux, D. Dubois and H. Prade. Representations of Uncertainty in Artificial Intelligence: Beyond Probability and Possibility. In P. Marquis, O. Papini and H. Prade (Eds),“A Guided Tour of Artificial Intelligence Research”, Volume 1, Chapter 4, Springer Verlag, pages 119-150, 2020. pdf
  3. F. Pichon, D. Dubois and T. Denoeux. Quality of Information Sources in Information Fusion. In E. Bossé and G. Rogova (Eds), “Information Quality in Information Fusion and Decision Making”, Chapter 2, Springer Verlag, Pages 31–49, 2019. pdf
  4. T. Denoeux. Quantifying Predictive Uncertainty Using Belief Functions: Different Approaches and Practical Construction. In V. Kreinovich, S. Sriboonchitta and N. Chakpitak (Eds), Predictive Econometrics and Big Data, Springer-Verlag, SCI 753, pages 157-176, 2018. pdf
  5. S. Benferhat, T. Denoeux, D. Dubois and H. Prade. Représentations de l’incertitude en intelligence artificielle. In P. Marquis, O. Papini and H. Prade (Eds), Panorama de l'Intelligence Artificielle, Volume 1: Représentation des connaissances et formalisation des raisonnements. Cépaduès Editions, Toulouse, France, Chapitre 3, Pages 65-121, 2014. pdf
  6. T. Denoeux. Statistical inference from ill-known data using belief functions. In V.-N. Huyn et al. (Eds), Uncertainty Analysis in Econometrics with Applications, Springer-Verlag, AISC 200, pages 33-48, 2013. pdf
  7. T. Denoeux and M.-H. Masson. Dempster-Shafer reasoning in large partially ordered sets: Applications in Machine Learning. In V.-N. Huyn, Y. Nakamori, J. Lawry and M. Inuigushi (Eds), Integrated Uncertainty Management and Applications, Springer-Verlag, AISC 69, pages 39-54, 2010. pdf
  8. D. Mercier, T. Denoeux and M.-H. Masson. Belief function correction mechanisms. In B. Bouchon-Meunier, R.R. Yager, J.-L. Verdegay, M. Ojeda-Aciego and L. Magdalena (Eds), Foundations of Reasoning under Uncertainty, Springer-Verlag, Studies in Fuzziness and Soft Computing 249:203–222, 2010. pdf
  9. G. Manson, K. Worden,S. G. Pierce, T Denoeux. Uncertainty analysis. Encyclopedia of Structural Health Monitoring, Wiley, 2009. pdf
  10. V. Cherfaoui, T. Denoeux and Z. L. Cherfi. Confidence management in vehicular networks (chap. 13). In H. Moustafa and Y. Zhang (Eds), Vehicular Networks: Techniques, Standards and Applications, CRC Press, pages 355-377, 2009. pdf
  11. A. Ben Yaghlane, T. Denoeux and K. Mellouli. Elicitation of Expert Opinions for Constructing Belief Functions. In B. Bouchon-Meunier, C. Marsala, M. Rifqi and R. R Yager (Eds), Uncertainty and Intelligent Information Systems, World Scientific, pages 75-89, 2008. pdf
  12. T. Denoeux, M.-H. Masson. Clustering of proximity data using belief functions. In B. Bouchon-Meunier, L. Foulloy and R. R. Yager, Eds, Intelligent Systems for Information Processing: From representation to applications, Elsevier, Amsterdam, pages 291-302, 2003. pdf
  13. J. François, Y. Grandvalet, T. Denoeux, J.-M. Roger. Bagging improves uncertainty representation in evidential pattern classification. In B. Bouchon-Meunier, J. Gutiérrez-Rios, L. Magdalena and R. Y. Yager , Eds, Technologies for Constructing Intelligent Systems 1 - Tasks, Physica-Verlag, Heidelberg, pages 295-308, 2002. pdf
  14. T. Denoeux. Diagnostic par reconnaissance de formes : approches non probabilistes. In B. Dubuisson, Ed, Diagnostic, Intelligence Artificielle et Reconnaissance de Formes , Traité IC2, Hermès, Paris, chapitre 3, pages 143-178, 2001. pdf
  15. T. Denoeux. Génération de règles par apprentissage contraint d'un perceptron multicouche. In E. Diday, Y. Kodratoff, P. Brito and M. Moulet, Eds, Induction symbolique et numérique à partir de données, Cépaduès-Editions, Toulouse, pages 435-445, 2000.
  16. T. Denoeux. Allowing imprecision in belief representation using fuzzy-valued belief structures. In B. Bouchon-Meunier, R. Y. Yager and L. A. Zadeh, Eds, Information, Uncertainty and Fusion, Kluwer Academic Publisher, Boston, pages 269-281, 2000. pdf
  17. J. M. Boisseau, B. Monier, O. Lefèvre, T. Denoeux and X. Ding. Highway Traffic forecasting using Artificial Neural Networks. In F. Fogelman-Soulié and P. Gallinari, Eds, Industrial Applications of Neural Networks, World Scientific, Singapore, pages 223-231, 1998.
  18. T. Denoeux. Pattern Classification. In E. Fiesler and R. Beale, editors, Handbook of Neural Computation. Oxford University Press and Institute of Physics Publishing, pages F1.2:1-8, 1996. pdf
  19. X. Ding, S. Canu, and T. Denoeux. Neural network based models for forecasting. In J. G. Taylor, editor, Neural Networks and their Applications, John Wiley and Sons, Chichester, pages 153-167, 1996.
  20. T. Denoeux. Les procédures d'apprentissage connexionnistes (traduction et commentaire de l'article de G.I. Hinton). In R. S. Michalski, J. G. Carbonell, T. M. Mitchell, and Y. Kodratoff, editors, Apprentissage Symbolique: Une approche de l'Intelligence Artificielle, pages 471-533. Cepaduès-Editions, Toulouse, 1993.

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