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**Instructor: | **Instructor: | ||
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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:cmu_2017_lecture1.pdf|Representation and combination of evidence}} | + | - {{ :en:bf2023_lecture1.pdf |Belief functions on finite frames. Dempster' |
- | - {{:en:cmu_2017_classification.pdf|Classification}} | + | - {{ :en:bf2023_lecture2.pdf |Decision analysis and classification}} |
- | - {{: | + | - {{ :en:bf2023_lecture3.pdf |Multinomial predictive belief functions}} |
- | - {{:en:cmu_2017_clustering.pdf|Clustering}} | + | - {{ :en:bf2023_lecture4.pdf |Statistical |
- | - {{:en:cmu_2017_statistical_inference.pdf|Statistical | + | |
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**Exercises** | **Exercises** | ||
- | - {{:en:ex_lecture1.pdf|Representation and combination of evidence}} | + | - {{ :en:bf2023_ex_lecture1.pdf |Exercises on Chapter 1}} |
- | - {{:en:ex_classification.pdf|Classification}} | + | - {{ : |
- | - {{:en:ex_clustering.pdf|Clustering}} | + | - {{ :en:bf2023_ex_lecture2.pdf |Exercises on Chapter 2}} |
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+ | - {{ :en:bf2023_projets.pdf |Projects}} | ||
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**Papers** | **Papers** | ||
- | - {{: | + | - T. Denoeux. A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Transactions on Systems, Man and Cybernetics, |
- | - {{:en:kevclus.pdf|T. Denoeux, | + | - T. Denoeux. A neural network classifier based on Dempster-Shafer theory. IEEE Transactions on Systems, Man and Cybernetics A, 30(2): |
- | + | - T. Denoeux. Constructing belief functions from sample data using multinomial confidence regions. International Journal of Approximate Reasoning, Vol. 42, pages 228-252, 2006. {{ : | |
- | + | - O. Kanjanatarakul, T. Denoeux and S. Sriboonchitta. Prediction of future observations using belief functions: a likelihood-based approach. International Journal of Approximate Reasoning, Vol. 72, pages 71-94, 2016. {{ : | |
- | + | - Thierry Denoeux. Reasoning with fuzzy and uncertain evidence using epistemic random fuzzy sets: general framework and practical models. Fuzzy Sets and Systems, Vol. 453, pages 1–36, 2023. {{ : | |
- | **R code** | + | - T. Denoeux. Quantifying Prediction Uncertainty in Regression using Random Fuzzy Sets: the ENNreg model. IEEE Transactions on Fuzzy Systems (to appear), 2023. {{ : |
- | + | - T. Denoeux. Maximum likelihood estimation from Uncertain Data in the Belief Function Framework. IEEE Transactions on Knowledge and Data Engineering, | |
- | -{{:en:example1.txt|Behrens-Fisher example}} | + | - 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, |
**Data** | **Data** | ||
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