I. Baaj, Z. Bouraoui, A. Cornuéjols, T. Denoeux, S. Destercke, D. Dubois, M.-J. Lesot, J. Marques-Silva, J. Mengin, H. Prade, S. Schockaert, M. Serrurier, O. Strauss, C. Vrain. Synergies Between Machine Learning and Reasoning – An Introduction by the Kay R. Amel group. International Journal of Approximate Reasoning, 109206, 2024. pdf

E. Rammaso, T. Denoeux and G. Chevallier. Clustering acoustic emission data streams with sequentially appearing clusters using mixture models. Mechanical Systems and Signal Processing, Vol. 181, 109504, 2022. pdf

J. Liu, S. Sriboonchitta, A. Wiboonpongse and T. Denoeux. A trivariate Gaussian copula stochastic frontier model with sample selection. International Journal of Approximate Reasoning, Volume 137, Pages 181-198, 2021. pdf

S. Sriboonchitta, J. Liu, A. Wiboonpongse and T. Denoeux. A double-copula stochastic frontier model with dependent error components and correction for sample selection. International Journal of Approximate Reasoning, Volume 80, Pages 174-184, 2017. pdf

A. Wiboonpongse, J. Liu, S. Sriboonchitta and T. Denoeux. Modeling dependence between error components of the stochastic frontier model using copula: Application to Intercrop Coffee Production in Northern Thailand. International Journal of Approximate Reasoning, Vol. 65, Pages 34-44, 2015. pdf

E. Côme, L. Oukhellou, T. Denoeux and P. Aknin. Fault diagnosis of a railway device using semi-supervised independent factor analysis with mixing constraints. Pattern Analysis and Applications, Vol. 15, Number 3, pages 313-326, 2012. pdf

Z. Younes, F. Abdallah, T. Denoeux and H. Snoussi. A dependent multi-label classification method derived from the k-nearest neighbor rule. EURASIP Journal on Advances in Signal Processing, vol. 2011, Article ID 645964, 14 pages, 2011. doi:10.1155/2011/645964. pdf

T. Denoeux and G. Govaert. Un algorithme de classification automatique non paramétrique. Comptes-Rendus de l'Académie des Sciences , t. 324, Série I, p. 673-678, 1997. pdf

R. Lengellé and T. Denoeux. Training MLPs layer by layer using an objective function for internal representations. Neural Networks, 9:83-97, 1996. pdf

T. Denoeux and R. Lengellé. Initializing back-propagation networks with prototypes. Neural Networks, 6:351-363, 1993. pdf