UMR CNRS 7253

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Welcome

thierry_denoeux.jpg

Professor at Université de technologie de Compiègne
Department of Computer Science
Heudiasyc Laboratory (UMR CNRS 7253)

Senior member of Institut universitaire de France
Editor-in-Chief, International Journal of Approximate Reasoning

Director, Laboratory of Excellence MS2T
President, Belief Functions and Applications Society

News:

  • Interview on INS2I web site
  • Version 2.0.0 of the R package 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: "T. Denoeux, Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspective. Knowledge-Based Systems 176:54-67, 2019").
  • Version 1.0.0 of the R package evreg has been released on CRAN. This new package implements the 'Evidential Neural Network for Regression' (ENNreg) model recently introduced in Denoeux (2023a). In this model, prediction uncertainty is quantified by Gaussian random fuzzy numbers as introduced in 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).


Contact information

Prof. Thierry Denoeux
Université de Technologie de Compiègne
UMR CNRS 7253 Heudiasyc
Rue Roger Couttolenc
CS 60319
60203 Compiègne Cedex
France

email: tdenoeux[at]utc.fr
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