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====== Belief Functions and Machine Learning ====== | ====== Belief Functions and Machine Learning ====== | ||
- | - Thierry Denoeux. An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers. Belief Functions: Theory and Applications, 13506, Springer International Publishing, pp.57-66, | + | |
- | - Andrea Campagner, Davide Ciucci, Thierry Denoeux. A Distributional Approach for Soft Clustering Comparison and Evaluation. Belief Functions: Theory and Applications, 13506, Springer International Publishing, pp.3-12, 2022, Lecture Notes in Computer Science, | + | - 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. {{ : |
- | - H. Fu, X. Yue, W. Liu and T. Denoeux, " | + | |
+ | - Andrea Campagner, Davide Ciucci, Thierry Denoeux. A Distributional Approach for Soft Clustering Comparison and Evaluation. | ||
+ | - 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, |