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Thierry Denoeux
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Publications
- 2023
- [j121]Thierry Denoeux:
Reasoning with fuzzy and uncertain evidence using epistemic random fuzzy sets: General framework and practical models. Fuzzy Sets Syst. 453: 1-36 (2023) - [j120]Thierry Denoeux:
Parametric families of continuous belief functions based on generalized Gaussian random fuzzy numbers. Fuzzy Sets Syst. 471: 108679 (2023) - [j115]Thierry Denoeux:
Quantifying Prediction Uncertainty in Regression Using Random Fuzzy Sets: The ENNreg Model. IEEE Trans. Fuzzy Syst. 31(10): 3690-3699 (2023) - [c105]Thierry Denoeux:
Belief Functions on the Real Line Defined by Transformed Gaussian Random Fuzzy Numbers. FUZZ 2023: 1-6 - 2022
- [c102]Thierry Denoeux:
An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers. BELIEF 2022: 57-66 - [i20]Thierry Denoeux:
Reasoning with fuzzy and uncertain evidence using epistemic random fuzzy sets: general framework and practical models. CoRR abs/2202.08081 (2022) - [i17]Thierry Denoeux:
An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers. CoRR abs/2208.00647 (2022) - 2021
- [j108]Thierry Denoeux:
Belief functions induced by random fuzzy sets: A general framework for representing uncertain and fuzzy evidence. Fuzzy Sets Syst. 424: 63-91 (2021) - [j105]Thierry Denoeux:
Distributed combination of belief functions. Inf. Fusion 65: 179-191 (2021) - [j104]Thierry Denoeux:
NN-EVCLUS: Neural network-based evidential clustering. Inf. Sci. 572: 297-330 (2021) - 2020
- [j98]Thierry Denoeux:
Calibrated model-based evidential clustering using bootstrapping. Inf. Sci. 528: 17-45 (2020) - [i8]Thierry Denoeux:
Belief functions induced by random fuzzy sets: Application to statistical inference. CoRR abs/2004.11638 (2020) - [i7]Thierry Denoeux:
NN-EVCLUS: Neural Network-based Evidential Clustering. CoRR abs/2009.12795 (2020) - 2019
- [j97]Thierry Denoeux:
Editorial: Opening up computer science. Array 1-2: 100005 (2019) - [j96]Thierry Denoeux:
Decision-making with belief functions: A review. Int. J. Approx. Reason. 109: 87-110 (2019) - [j94]Thierry Denoeux:
Logistic regression, neural networks and Dempster-Shafer theory: A new perspective. Knowl. Based Syst. 176: 54-67 (2019) - [i5]Thierry Denoeux:
Calibrated model-based evidential clustering using bootstrapping. CoRR abs/1912.06137 (2019) - 2018
- [c87]Thierry Denoeux:
Logistic Regression Revisited: Belief Function Analysis. BELIEF 2018: 57-64 - [p4]Thierry Denoeux:
Quantifying Predictive Uncertainty Using Belief Functions: Different Approaches and Practical Construction. Predictive Econometrics and Big Data 2018: 157-176 - [i2]Thierry Denoeux:
Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspective. CoRR abs/1807.01846 (2018) - [i1]Thierry Denoeux:
Decision-Making with Belief Functions: a Review. CoRR abs/1808.05322 (2018) - 2016
- [j79]Thierry Denoeux:
40 years of Dempster-Shafer theory. Int. J. Approx. Reason. 79: 1-6 (2016) - 2014
- [j63]Thierry Denoeux:
Likelihood-based belief function: Justification and some extensions to low-quality data. Int. J. Approx. Reason. 55(7): 1535-1547 (2014) - [j62]Thierry Denoeux:
Rejoinder on "Likelihood-based belief function: Justification and some extensions to low-quality data". Int. J. Approx. Reason. 55(7): 1614-1617 (2014) - 2013
- [j56]Thierry Denoeux:
Maximum Likelihood Estimation from Uncertain Data in the Belief Function Framework. IEEE Trans. Knowl. Data Eng. 25(1): 119-130 (2013) - 2011
- [j49]Thierry Denoeux:
Maximum likelihood estimation from fuzzy data using the EM algorithm. Fuzzy Sets Syst. 183(1): 72-91 (2011) - 2010
- [c40]Thierry Denoeux:
Theory of Belief Functions for Data Analysis and Machine Learning Applications: Review and Prospects. KSEM 2010: 3 - [c39]Thierry Denoeux:
Maximum Likelihood from Evidential Data: An Extension of the EM Algorithm. SMPS 2010: 181-188 - 2009
- [j40]Thierry Denoeux:
Extending stochastic ordering to belief functions on the real line. Inf. Sci. 179(9): 1362-1376 (2009) - 2008
- [j37]Thierry Denoeux:
Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence. Artif. Intell. 172(2-3): 234-264 (2008) - [j36]Thierry Denoeux:
Special issue in memory of Philippe Smets (1938-2005). Int. J. Approx. Reason. 48(2): 349-351 (2008) - [p1]Thierry Denoeux:
A k -Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory. Classic Works of the Dempster-Shafer Theory of Belief Functions 2008: 737-760 - 2007
- [c21]Thierry Denoeux:
Pattern Recognition and Information Fusion Using Belief Functions: Some Recent Developments. ECSQARU 2007: 1 - 2006
- [j27]Thierry Denoeux:
Constructing belief functions from sample data using multinomial confidence regions. Int. J. Approx. Reason. 42(3): 228-252 (2006) - [c16]Thierry Denoeux:
The cautious rule of combination for belief functions and some extensions. FUSION 2006: 1-8 - 2005
- [j24]Thierry Denoeux:
R. P. Srivastava and T. J. Mock, Belief Functions in Business Decisions, in Studies in Fuzziness and Soft Computing, vol. 88, Physica-Verlag, Heidelberg (2002) ISBN 3-7908-1451-2 (345pp.). Fuzzy Sets Syst. 151(2): 435-436 (2005) - 2001
- [j11]Thierry Denoeux:
Inner and Outer Approximation of Belief Structures Using a Hierarchical Clustering Approach. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 9(4): 437-460 (2001) - 2000
- [j10]Thierry Denoeux:
Modeling vague beliefs using fuzzy-valued belief structures. Fuzzy Sets Syst. 116(2): 167-199 (2000) - [j8]Thierry Denoeux:
A neural network classifier based on Dempster-Shafer theory. IEEE Trans. Syst. Man Cybern. Part A 30(2): 131-150 (2000) - 1999
- [j7]Thierry Denoeux:
Reasoning with imprecise belief structures. Int. J. Approx. Reason. 20(1): 79-111 (1999) - 1997
- [j5]Thierry Denoeux:
Analysis of evidence-theoretic decision rules for pattern classification. Pattern Recognit. 30(7): 1095-1107 (1997) - [c4]Thierry Denoeux:
Function approximation in the framework of evidence theory: a connectionist approach. ICNN 1997: 199-203 - 1995
- [j2]Thierry Denoeux:
A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Trans. Syst. Man Cybern. 25(5): 804-813 (1995)
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