This shows you the differences between two versions of the page.
Both sides previous revisionPrevious revision | |||
en:publi:belief_art [2024/04/08 05:49] – tdenoeux | en:publi:belief_art [2024/04/09 05:11] (current) – tdenoeux | ||
---|---|---|---|
Line 28: | Line 28: | ||
- T. Denoeux. Uncertainty Quantification in Logistic Regression using Random Fuzzy Sets and Belief Functions. International Journal of Approximate Reasoning, Volume 168, 109159, 2024. {{ : | - T. Denoeux. Uncertainty Quantification in Logistic Regression using Random Fuzzy Sets and Belief Functions. International Journal of Approximate Reasoning, Volume 168, 109159, 2024. {{ : | ||
- | - Ying Lv, Bofeng Zhangh, Xiaodong Yue, Thierry Denoeux, Shan Yue. Selecting Reliable Instances Based on Evidence Theory for Transfer Learning. Expert Systems with Applications, | + | - Ying Lv, Bofeng Zhangh, Xiaodong Yue, Thierry Denoeux, Shan Yue. Selecting Reliable Instances Based on Evidence Theory for Transfer Learning. Expert Systems with Applications, |
- T. Denoeux. Quantifying Prediction Uncertainty in Regression using Random Fuzzy Sets: the ENNreg model. IEEE Transactions on Fuzzy Systems, Vol. 31, Issue 10, pages 3690-3699, 2023. {{ : | - T. Denoeux. Quantifying Prediction Uncertainty in Regression using Random Fuzzy Sets: the ENNreg model. IEEE Transactions on Fuzzy Systems, Vol. 31, Issue 10, pages 3690-3699, 2023. {{ : | ||
- Z. Tong, Ph. Xu and T. Denoeux. An evidential classifier based on Dempster-Shafer theory and deep learning. Neurocomputing, | - Z. Tong, Ph. Xu and T. Denoeux. An evidential classifier based on Dempster-Shafer theory and deep learning. Neurocomputing, |