This shows you the differences between two versions of the page.
Both sides previous revisionPrevious revisionNext revision | Previous revisionLast revisionBoth sides next revision | ||
en:bf [2017/07/31 05:41] – tdenoeux | en:bf [2023/09/28 11:15] – 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:cmu_2017_lecture1.pdf|Representation | + | - {{ :en:bf2023_lecture1.pdf |Belief functions on finite frames. Dempster' |
- | - {{:en:cmu_2017_classification.pdf|Classification}} | + | - {{ : |
+ | - {{ : | ||
+ | - {{ :en:bf2023_lecture4.pdf |Statistical inference}} | ||
+ | |||
+ | |||
**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:bf2023_projets.pdf |Projects}} | ||
+ | |||
**Papers** | **Papers** | ||
- | - T. Denoeux. A neural network classifier based on Dempster-Shafer theory. IEEE transactions | + | |
- | + | | |
+ | - 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, | ||
+ | - 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. {{ : | ||
+ | - 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, | ||
+ | - 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** | ||
- | **R code** | + | -{{: |
- | + | -{{:en:globalization_2017_short.xlsx.zip|Globalization dataset (full)}} | |
- | -{{:en:example1.txt|Behrens-Fisher example}} | + | |
+ | -{{ : | ||
- |