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en:publi:befief_conf [2021/12/20 15:01] – tdenoeux | en:publi:befief_conf [2023/08/06 05:39] – tdenoeux | ||
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====== Belief Functions and Machine Learning ====== | ====== Belief Functions and Machine Learning ====== | ||
+ | - Thierry Denoeux. Belief Functions on the Real Line defined by Transformed Gaussian Random Fuzzy Numbers. 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2023), Songdo Incheon, Korea, August 13-17, 2023. {{ : | ||
+ | - Thierry Denoeux. An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers. In: Le Hégarat-Mascle, | ||
+ | - Andrea Campagner, Davide Ciucci, Thierry Denoeux. A Distributional Approach for Soft Clustering Comparison and Evaluation. In: Le Hégarat-Mascle, | ||
+ | - H. Fu, X. Yue, W. Liu and T. Denoeux, " | ||
- Wei Liu, Xiaodong Yue, Yufei Chen and Thierry Denoeux. Trusted Multi-View Deep Learning with Opinion Aggregation. In Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22), Vancouver, Canada, February 22-March 1, 2022. {{ : | - Wei Liu, Xiaodong Yue, Yufei Chen and Thierry Denoeux. Trusted Multi-View Deep Learning with Opinion Aggregation. In Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22), Vancouver, Canada, February 22-March 1, 2022. {{ : | ||
- Zh. Tong, Ph. Xu and T. Denoeux. Fusion of Evidential CNN Classifiers for Image Classification. In T. Denoeux, E. Lefèvre, Zh. Liu and F. Pichon (Eds), Belief Functions: Theory and Applications, | - Zh. Tong, Ph. Xu and T. Denoeux. Fusion of Evidential CNN Classifiers for Image Classification. In T. Denoeux, E. Lefèvre, Zh. Liu and F. Pichon (Eds), Belief Functions: Theory and Applications, | ||
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- M. Rombaut, I. Jarkass et T. Denoeux. State recognition in discrete dynamical systems using Petri nets and evidence theory. In A. Hunter and S. Pearsons (Eds), Symbolic and quantitative approaches to reasoning and uncertainty (ECSQARU' | - M. Rombaut, I. Jarkass et T. Denoeux. State recognition in discrete dynamical systems using Petri nets and evidence theory. In A. Hunter and S. Pearsons (Eds), Symbolic and quantitative approaches to reasoning and uncertainty (ECSQARU' | ||
- S. Petit-Renaud et T. Denoeux. Handling different forms of uncertainty in regression analysis: a fuzzy belief structure approach. In A. Hunter and S. Pearsons (Eds), Symbolic and quantitative approaches to reasoning and uncertainty (ECSQARU' | - S. Petit-Renaud et T. Denoeux. Handling different forms of uncertainty in regression analysis: a fuzzy belief structure approach. In A. Hunter and S. Pearsons (Eds), Symbolic and quantitative approaches to reasoning and uncertainty (ECSQARU' | ||
- | - T. Denoeux. Function approximation in the framework of evidence theory: A connectionist approach. Proceedings of the 1997 International Conference on Neural Networks (ICNN' | + | - T. Denoeux. Function approximation in the framework of evidence theory: A connectionist approach. Proceedings of the 1997 International Conference on Neural Networks (ICNN' |
- L. M. Zouhal and T. Denoeux. Generalizing the evidence-theoretic k-NN rule to fuzzy pattern recognition. Proceedings of the Second International Symposium on Fuzzy Logic and Applications ISFL' | - L. M. Zouhal and T. Denoeux. Generalizing the evidence-theoretic k-NN rule to fuzzy pattern recognition. Proceedings of the Second International Symposium on Fuzzy Logic and Applications ISFL' | ||
- L. M. Zouhal and T. Denoeux. Reconnaissance de Formes Floues par la Théorie de Dempster et Shafer. In Rencontres Francophones sur la Logique Floue et ses Applications, | - L. M. Zouhal and T. Denoeux. Reconnaissance de Formes Floues par la Théorie de Dempster et Shafer. In Rencontres Francophones sur la Logique Floue et ses Applications, |