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- | ====== Belief Functions and Pattern Recognition | + | ====== Belief Functions and Machine Learning |
- | - Zheng Tong, Philippe Xu, and Thierry | + | - T. Denoeux and V. Kreinovich. Algebraic Product Is the Only “And-like” Operation for Which Normalized Intersection Is Associative: |
+ | - 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. {{ : | ||
+ | - 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, | ||
+ | -B. Yuan, X. Yue, Y. Lv and T. Denoeux. Evidential Deep Neural Networks for Uncertain Data Classification. In G. Li et al. (Eds), Knowledge Science, Engineering and Management part II (Proceedings of KSEM 2020), Springer, LNAI 12275, Hangzhou, China, August 28–30, pages 427-437, 2020. {{ : | ||
+ | - Zheng Tong, Philippe Xu, and Thierry | ||
- T. Denoeux and P. Shenoy. An Axiomatic Utility Theory for Dempster-Shafer Belief Functions. International Symposium on Imprecise Probabilities: | - T. Denoeux and P. Shenoy. An Axiomatic Utility Theory for Dempster-Shafer Belief Functions. International Symposium on Imprecise Probabilities: | ||
- L. Ma and T. Denoeux. Making Set-valued Predictions in Evidential Classification: | - L. Ma and T. Denoeux. Making Set-valued Predictions in Evidential Classification: | ||
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- C. Lian, S. Ruan and T. Denoeux. Joint feature transformation and selection based on dempster-shafer theory. 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU-2016), | - C. Lian, S. Ruan and T. Denoeux. Joint feature transformation and selection based on dempster-shafer theory. 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU-2016), | ||
- Ph. Xu, F. Davoine and T. Denœux. Evidential Multinomial Logistic Regression for Multiclass Classifier Calibration. In Proceedings of the 18th International Conference on Information Fusion, pages 1106-1112, Washington, D.C., July 6-9, 2015. {{: | - Ph. Xu, F. Davoine and T. Denœux. Evidential Multinomial Logistic Regression for Multiclass Classifier Calibration. In Proceedings of the 18th International Conference on Information Fusion, pages 1106-1112, Washington, D.C., July 6-9, 2015. {{: | ||
- | - L. Jiao, T. Denoeux and Q. Pan. Evidential Editing K-Nearest Neighbor Classifier. In S. Destercke and T. Denoeux (Rds), Symbolic and Quantitative Approaches to Reasoning with Uncertainty, | + | - L. Jiao, T. Denoeux and Q. Pan. Evidential Editing K-Nearest Neighbor Classifier. In S. Destercke and T. Denoeux (Eds), Symbolic and Quantitative Approaches to Reasoning with Uncertainty, |
- O. Kanjanatarakul, | - O. Kanjanatarakul, | ||
- S. Leurcharusmee, | - S. Leurcharusmee, | ||
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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, |