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, 2014. pdf

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

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

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

G. Manson, K. Worden,S. G. Pierce, T Denoeux. Uncertainty analysis. Encyclopedia of Structural Health Monitoring, Wiley, 2009. pdf

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

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

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

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

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

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.

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

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.

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

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.

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.