This shows you the differences between two versions of the page.
Both sides previous revisionPrevious revisionNext revision | Previous revisionNext revisionBoth sides next revision | ||
en:bf [2022/09/06 13:58] – tdenoeux | en:bf [2023/09/05 14:07] – tdenoeux | ||
---|---|---|---|
Line 1: | Line 1: | ||
- | ====== | + | ====== |
**Instructor: | **Instructor: | ||
Line 5: | Line 5: | ||
Description: | Description: | ||
+ | ** Course outline ** | ||
+ | - Belief functions on finite frames | ||
+ | - Decision analysis | ||
+ | - Evidential k-NN classifier | ||
+ | - Evidential neural network classifier | ||
+ | - Predictive belief functions for categorical and ordinal variables | ||
+ | - Random sets and belief functions in a general mathematical framework | ||
+ | - Possibility theory and epistemic random fuzzy sets | ||
+ | - Statistical prediction using belief functions: application to linear and logistic regression | ||
+ | - The ENNreg model | ||
+ | - Uncertain data and the evidential EM algorithm | ||
**Slides** | **Slides** | ||
- | - {{ :en:bf2022_lecture1.pdf |Belief functions on finite frames. Dempster' | + | - {{ :en:bf2023_lecture1.pdf |Belief functions on finite frames. Dempster' |
- | - {{ :en:bf2022_lecture2.pdf |Decision analysis}} | + | - {{ :en:bf2023_lecture2.pdf |Decision analysis |
- | - {{ :en:bf2022_clustering.pdf |Evidential clustering}} | + | - {{ :en:bf2023_lecture3.pdf |Multinomial predictive belief functions}} |
- | - {{ :en:bf2022_inference.pdf |Statistical inference}} | + | - {{ :en:bf2023_lecture4.pdf |Statistical inference}} |
+ | |||
- | **Lecture notes** | ||
- | - {{ : | ||
- | |||
- | **Videos** | ||
- | |||
- | - [[https:// | ||
- | - [[https:// | ||
- | - [[https:// | ||
- | - [[https:// | ||
- | - [[https:// | ||
- | - [[https:// | ||
- | - [[https:// | ||
- | - [[https:// | ||
- | - [[https:// | ||
**Exercises** | **Exercises** | ||
- | - {{ :en:bf2022_ex_lecture1.pdf |Exercises | + | - {{ :en:bf2023_ex_lecture1.pdf |Exercises |
- | - {{ :en:bf2022_ex_lecture1sol.pdf |Solutions}} | + | - {{ :en:bf2023_ex_lecture1sol.pdf |Solutions}} |
- | - {{ :en:bf2022_ex_lecture2.pdf |Exercise of lecture | + | - {{ :en:bf2023_ex_lecture2.pdf |Exercises on Chapter |
- | - {{ :en:bf2022_ex_lecture2sol.pdf |Solution}} | + | - {{ :en:bf2023_ex_lecture2sol.pdf |Solutions}} |
- | - {{ :en:bf2022_ex_lecture3.pdf |Project of lecture 3}} | + | - {{ :en:bf2023_ex_classification.pdf |Exercises on classification}} |
- | - {{ :en:bf2022_ex_clustering.pdf |Project | + | - {{ : |
- | - {{ :en:clustering_ts.pdf |Solution}} | + | - {{ :en:bf2023_ex_pbf.pdf |Exercise |
- | - {{ :en:bf2022_ex_inference.pdf |Exercises on statistical inference}} | + | - {{ :en:bf2023_ex_pbf_sol.pdf |Solutions}} |
+ | - {{ :en:bf2023_ex_inference.pdf |Exercises on statistical inference}} | ||
+ | - {{ : | ||
+ | |||
**Papers** | **Papers** | ||
- | - T. Denoeux. A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Transactions on Systems, Man and Cybernetics, | + | - T. Denoeux. A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Transactions on Systems, Man and Cybernetics, |
- | - L. M. Zouhal and T. Denoeux. An evidence-theoretic k-NN rule with parameter optimization. IEEE Transactions on Systems, Man and Cybernetics - Part C, 28(2): | + | |
- T. Denoeux. A neural network classifier based on Dempster-Shafer theory. IEEE Transactions on Systems, Man and Cybernetics A, 30(2): | - T. Denoeux. A neural network classifier based on Dempster-Shafer theory. IEEE Transactions on Systems, Man and Cybernetics A, 30(2): | ||
- | - T. Denoeux, S. Sriboonchitta and O. Kanjanatarakul. Evidential clustering | + | - T. Denoeux. |
- | - T. Denoeux. NN-EVCLUS: Neural Network-based Evidential Clustering. Information Sciences, Vol. 572, Pages 297-330, 2021. {{ : | + | - O. Kanjanatarakul, T. Denoeux |
- | - T. Denoeux. | + | - Thierry |
- | - T. Denoeux. Likelihood-based | + | - T. Denoeux. |
- | - N. Ben Abdallah, N. Mouhous-Voyneau and T. Denoeux. | + | |
- | - O. Kanjanatarakul, | + | |
- | - Ph. Xu, F. Davoine, H. Zha and T. Denoeux. Evidential calibration of binary SVM classifiers. International Journal of Approximate Reasoning, Vol 72, pages 55-70, 2016. {{: | + | |
- T. Denoeux. Maximum likelihood estimation from Uncertain Data in the Belief Function Framework. IEEE Transactions on Knowledge and Data Engineering, | - T. Denoeux. Maximum likelihood estimation from Uncertain Data in the Belief Function Framework. IEEE Transactions on Knowledge and Data Engineering, | ||
- | - B. Quost, T. Denoeux and S. Li. Parametric Classification with Soft Labels using the Evidential EM Algorithm. Linear Discriminant Analysis vs. Logistic Regression. Advances in Data Analysis and Classification, | + | - B. Quost, T. Denoeux and S. Li. Parametric Classification with Soft Labels using the Evidential EM Algorithm. Linear Discriminant Analysis vs. Logistic Regression. Advances in Data Analysis and Classification, |
- | - Thierry Denoeux. Reasoning with fuzzy and uncertain evidence using epistemic random fuzzy sets: general framework and practical models. Fuzzy Sets and Systems (to appear), 2022. {{ : | + | |
- | + | ||
**Data** | **Data** | ||
Line 62: | Line 57: | ||
-{{: | -{{: | ||
-{{ : | -{{ : | ||
+ | -{{ : | ||