212 citations · 850 across the 18 of their papers we have counts for
6 papers · 1 filter
An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers
Thierry Denoeux
We introduce a distance-based neural network model for regression, in which prediction uncertainty is quantified by a belief function on the real line. The model interprets the dis…
A Distributional Approach for Soft Clustering Comparison and Evaluation
Andrea Campagner, Davide Ciucci, Thierry Denœux
The development of external evaluation criteria for soft clustering (SC) has received limited attention: existing methods do not provide a general approach to extend comparison mea…
EGMM: an Evidential Version of the Gaussian Mixture Model for Clustering
Lianmeng Jiao, Thierry Denoeux, Zhun-ga Liu +1
The Gaussian mixture model (GMM) provides a simple yet principled framework for clustering, with properties suitable for statistical inference. In this paper, we propose a new mode…
NN-EVCLUS: Neural Network-based Evidential Clustering
Thierry Denoeux
Evidential clustering is an approach to clustering based on the use of Dempster-Shafer mass functions to represent cluster-membership uncertainty. In this paper, we introduce a neu…
Calibrated model-based evidential clustering using bootstrapping
Thierry Denoeux
Evidential clustering is an approach to clustering in which cluster-membership uncertainty is represented by a collection of Dempster-Shafer mass functions forming an evidential pa…
Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspective
Thierry Denoeux
We revisit logistic regression and its nonlinear extensions, including multilayer feedforward neural networks, by showing that these classifiers can be viewed as converting input o…