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--- en:publi:befief_conf [2019/12/16 11:00] – tdenoeux
+++ en:publi:befief_conf [2024/02/03 08:53] (current) – tdenoeux
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-====== Belief Functions and Pattern Recognition ======
+====== Belief Functions and Machine Learning ======
-  - Zheng Tong, Philippe Xu, and Thierry  Denoeux. ConvNet and Dempster-Shafer Theory for Object Recognition. In N. Ben Amor, B. Quost and M. Theobald (Eds), Scalable Uncertainty Management, Springer International Publishing, Cham, pages 368-381, 2019. {{ :en:publi:r3_zheng_sum2019_convnet_bf_classifier.pdf |pdf}}
+  - T. Denoeux and V. Kreinovich. Algebraic Product Is the Only “And-like” Operation for Which Normalized Intersection Is Associative: A Proof. Fifth International Conference on Artificial Intelligence and Computational Intelligence (AICI 2024), Hanoi, Vietnam, January 13-14, 2024. {{ :en:publi:tr23-49v3.pdf |pdf}}
+  - 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. {{ :en:publi:fuzzieee23_final.pdf |pdf}}
+  - Thierry Denoeux. An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers. In: Le Hégarat-Mascle, S., Bloch, I., Aldea, E. (eds) Belief Functions: Theory and Applications. BELIEF 2022. Lecture Notes in Computer Science, vol 13506. Springer, Cham, 2022, pp.57-66 {{ :en:publi:belief2022_rfs_v2.pdf |pdf}}
+  - Andrea Campagner, Davide Ciucci, Thierry Denoeux. A Distributional Approach for Soft Clustering Comparison and Evaluation. In: Le Hégarat-Mascle, S., Bloch, I., Aldea, E. (eds) Belief Functions: Theory and Applications. BELIEF 2022. Lecture Notes in Computer Science, vol 13506. Springer, Cham, 2022, pp. 3-12. {{ :en:publi:belief2022_paper_8971.pdf |pdf}}
+  - H. Fu, X. Yue, W. Liu and T. Denoeux, "Stable Clustering Ensemble Based on Evidence Theory," 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France, 16-19 October 2022, pp. 2046-2050. {{ :en:publi:sceevt-final.pdf |pdf}}
+  - 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. {{ :en:publi:trusted_aaai_submit.pdf |pdf}}
+  - 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, Springer International Publishing, Cham, pages 168-176, 2021. {{ :en:publi:r1-clean-e-fusion-dl_td.pdf |pdf}}
+  -B. Yuan, X. Yue, Y. Lv and T. Denoeux. Evidential Deep Neural Networks for Uncertain Data Classification. In G. Li et al. (Eds), Knowledge Science, Engineering and Management part II (Proceedings of KSEM 2020), Springer, LNAI 12275, Hangzhou, China, August 28–30, pages 427-437, 2020. {{ :en:publi:p157.pdf |pdf}}
+  - Zheng Tong, Philippe Xu, and Thierry  Denoeux. ConvNet and Dempster-Shafer Theory for Object Recognition. In N. Ben Amor, B. Quost and M. Theobald (Eds), Scalable Uncertainty Management, Springer International Publishing, Cham, pages 368-381, 2019. {{ :en:publi:r3_zheng_sum2019_convnet_bf_classifier.pdf |pdf}}
   - T. Denoeux and P. Shenoy. An Axiomatic Utility Theory for Dempster-Shafer Belief Functions. International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA 2019), Ghent, Belgium, July 3-6, 2019. Proceedings of Machine Learning Research 103:145–155, 2019. {{ :en:publi:denoeux19.pdf |pdf}}
   - L. Ma and T. Denoeux. Making Set-valued Predictions in Evidential Classification: A Comparison of Different Approaches. International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA 2019), Ghent, Belgium, July 3-6, 2019. Proceedings of Machine Learning Research 103:276–285, 2019. {{ :en:publi:ma19.pdf |pdf}}
@@ Line 14: / Line 22: @@
   - C. Lian, S. Ruan and T. Denoeux. Joint feature transformation and selection based on dempster-shafer theory. 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU-2016), Part I, CCIS 610, pp. 253–261, Springer, Eindhoven, The Netherlands, June 2016. {{:en:publi:ipmu2016.pdf|pdf}}
   - Ph. Xu, F. Davoine and T. Denœux. Evidential Multinomial Logistic Regression for Multiclass Classifier Calibration. In Proceedings of the 18th International Conference on Information Fusion, pages 1106-1112, Washington, D.C., July 6-9, 2015. {{:en:publi:fusion2015.pdf|pdf}}
-  - L. Jiao, T. Denoeux and Q. Pan. Evidential Editing K-Nearest Neighbor Classifier. In S. Destercke and T. Denoeux (Rds), Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Proc. of the 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, pages 461-471, LNAI 9161, 2015. {{:en:publi:ecsqaru_2015_submission_46.pdf|pdf}}
+  - L. Jiao, T. Denoeux and Q. Pan. Evidential Editing K-Nearest Neighbor Classifier. In S. Destercke and T. Denoeux (Eds), Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Proc. of the 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, pages 461-471, LNAI 9161, 2015. {{:en:publi:ecsqaru_2015_submission_46.pdf|pdf}}
   - O. Kanjanatarakul, N. Kaewsompong, S. Sriboonchitta and T. Denoeux. Estimation and Prediction Using Belief Functions: Application to Stochastic Frontier Analysis. In V.-N. Huynh et al. (eds.), Econometrics of Risk, Studies in Computational Intelligence 583, Springer, pages 171-184, 2015. {{:en:publi:329767_1_en_12_chapter_onlinepdf.pdf|pdf}}
   - S. Leurcharusmee, J. Sirisrisakulchai, S. Sriboonchitta and T. Denoeux. The Classifier Chain Generalized Maximum Entropy Model for Multi-label Choice Problems. In V.-N. Huynh et al. (eds.), Econometrics of Risk, Studies in Computational Intelligence 583, Springer, pages 185-199, 2015. {{:en:publi:tes_fai_2015.pdf|pdf}}
@@ Line 105: / Line 113: @@
   - M. Rombaut, I. Jarkass et T. Denoeux. State recognition in discrete dynamical systems using Petri nets and evidence theory. In A. Hunter and S. Pearsons (Eds), Symbolic and quantitative approaches to reasoning and uncertainty (ECSQARU'99), pages 352-361, London, June 1999. Springer Verlag. {{en:congres:petri_ecsqaru99.ps|postscript}}
   - S. Petit-Renaud et T. Denoeux. Handling different forms of uncertainty in regression analysis: a fuzzy belief structure approach. In A. Hunter and S. Pearsons (Eds), Symbolic and quantitative approaches to reasoning and uncertainty (ECSQARU'99), pages 340-351, London, June 1999. {{en:congres:ecsqaru99_simon.ps|postscript}}
-  - T. Denoeux. Function approximation in the framework of evidence theory: A connectionist approach. Proceedings of the 1997 International Conference on Neural Networks (ICNN'97) , volume 1, pages 199-203, Houston, June 1997. IEEE. {{en:congres:icnn97.ps|postscript}}
+  - T. Denoeux. Function approximation in the framework of evidence theory: A connectionist approach. Proceedings of the 1997 International Conference on Neural Networks (ICNN'97) , volume 1, pages 199-203, Houston, June 1997. IEEE. {{ :en:publi:icnn97_new.pdf |pdf}}
   - L. M. Zouhal and T. Denoeux. Generalizing the evidence-theoretic k-NN rule to fuzzy pattern recognition. Proceedings of the Second International Symposium on Fuzzy Logic and Applications ISFL'97, pages 294-300, Zurich, February 1997. ICSC Academic Press. {{en:congres:isfl97.ps|postscript}}
   - L. M. Zouhal and T. Denoeux. Reconnaissance de Formes Floues par la Théorie de Dempster et Shafer. In Rencontres Francophones sur la Logique Floue et ses Applications, pages 3-8, Nancy, Decembre 1996. Cépaduès. {{en:congres:lfa96.ps|postscript}}

UMR CNRS 7253

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