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--- en:start [2021/10/28 09:07] – [Welcome] tdenoeux
+++ en:start [2023/11/16 08:49] (current) – tdenoeux
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-{{en:thierry_denoeux.jpg?350}}
+{{:en:denoeux_2015.jpg?150|}}
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 Senior member of [[http://www.iufrance.fr|Institut universitaire de France]]\\
 Editor-in-Chief,  [[https://www.journals.elsevier.com/international-journal-of-approximate-reasoning/|International Journal of Approximate Reasoning]]\\ \\
-Director, Laboratory of Excellence [[http://www.utc.fr/labexms2t|MS2T]]\\
 President, [[https://bfasociety.org|Belief Functions and Applications Society]]
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   * Interview on [[https://www.elsevier.com/connect/editors-update/editor-in-a-60-second-spotlight-thierry-denoeux|Elsevier Editor Update]]
   * Interview on [[https://ins2i.cnrs.fr/fr/cnrsinfo/thierry-denoeux-resoudre-des-problemes-dincertitude-au-coeur-de-modeles-informatiques|INS2I web site]]
-  * The 6th International Conference on Belief Functions ([[https://www.lgi2a.univ-artois.fr/events/belief2021/|BELIEF 2021]]) was held in Shanghai, China, on October 15-19, 2021.
+  * Version 2.0.0 of the R package [[https://CRAN.R-project.org/package=evclass |evclass]] has been released on CRAN. This version contains new functions to express the outputs of trained logistic regression, radial basis function or multi-layer perceptron classifiers as Dempster-Shafer mass functions. (These methods are based on an interpretation of the operations performed in neural networks as the combination of weights of evidence by Dempster's rule, see: {{ :en:publi:nnbelief_kbs_v2_clean.pdf |"T. Denoeux, Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspective. Knowledge-Based Systems 176:54-67, 2019"}}).
+  * Version 1.0.1 of the R package [[https://CRAN.R-project.org/package=evreg |evreg]] has been released on CRAN. This new package implements the 'Evidential Neural Network for Regression' (ENNreg) model recently introduced in [[https://www.techrxiv.org/articles/preprint/Quantifying_Prediction_Uncertainty_in_Regression_using_Random_Fuzzy_Sets_the_ENNreg_model/21791831/1|Denoeux (2023a)]]. In this model, prediction uncertainty is quantified by Gaussian random fuzzy numbers as introduced in [[https://doi.org/10.1016/j.fss.2022.06.004|Denoeux (2023b)]]. The package contains functions for training the network, tuning hyperparameters by cross-validation or the hold-out method, and making predictions. It also contains utilities for making calculations with Gaussian random fuzzy numbers (such as, e.g., computing the degrees of belief and plausibility of an interval, or combining Gaussian random fuzzy numbers).
+  * The [[https://www.bfasociety.org/BFTA2023/|6th School on Belief Functions and their Applications]] took place from Oct. 27 to Nov 1 at the Japan Advanced Institute of Science and Technology, Ishikawa, Japan.
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UMR CNRS 7253

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