default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDThierry Denoeux
Person information
- affiliation: Université de technologie de Compiègne, France
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2025
[j127]Ling Huang
, Su Ruan, Pierre Decazes, Thierry Denoeux:
Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation. Inf. Fusion 113: 102648 (2025)- 2024
- [j126]Ying Lv, Bofeng Zhang, Xiaodong Yue, Thierry Denoeux, Shan Yue:
Selecting reliable instances based on evidence theory for transfer learning. Expert Syst. Appl. 250: 123739 (2024) - [j125]Thierry Denoeux:
Uncertainty quantification in logistic regression using random fuzzy sets and belief functions. Int. J. Approx. Reason. 168: 109159 (2024) - [j124]Ismaïl Baaj, Zied Bouraoui, Antoine Cornuéjols, Thierry Denoeux, Sébastien Destercke, Didier Dubois, Marie-Jeanne Lesot, João Marques-Silva, Jérôme Mengin, Henri Prade, Steven Schockaert, Mathieu Serrurier, Olivier Strauss, Christel Vrain:
Synergies between machine learning and reasoning - An introduction by the Kay R. Amel group. Int. J. Approx. Reason. 171: 109206 (2024) - [j123]Thierry Denoeux:
Combination of dependent and partially reliable Gaussian random fuzzy numbers. Inf. Sci. 681: 121208 (2024) - [c108]Thierry Denoeux:
Uncertainty Quantification in Regression Neural Networks Using Likelihood-Based Belief Functions. BELIEF 2024: 40-48 - [c107]Ling Huang, Yucheng Xing, Thierry Denoeux, Mengling Feng:
An Evidential Time-to-Event Prediction Model Based on Gaussian Random Fuzzy Numbers. BELIEF 2024: 49-57 - [c106]Thierry Denoeux:
Combination of Dependent Gaussian Random Fuzzy Numbers. BELIEF 2024: 264-272 - [e11]Yaxin Bi, Anne-Laure Jousselme, Thierry Denoeux:
Belief Functions: Theory and Applications - 8th International Conference, BELIEF 2024, Belfast, UK, September 2-4, 2024, Proceedings. Lecture Notes in Computer Science 14909, Springer 2024, ISBN 978-3-031-67976-6 [contents] - [i23]Ling Huang, Yucheng Xing, Thierry Denoeux, Mengling Feng:
An evidential time-to-event prediction model based on Gaussian random fuzzy numbers. CoRR abs/2406.13487 (2024) - 2023
- [j122]Thierry Denoeux:
Reasoning with fuzzy and uncertain evidence using epistemic random fuzzy sets: General framework and practical models. Fuzzy Sets Syst. 453: 1-36 (2023) - [j121]Thierry Denoeux:
Parametric families of continuous belief functions based on generalized Gaussian random fuzzy numbers. Fuzzy Sets Syst. 471: 108679 (2023) - [j120]Andrea Campagner, Davide Ciucci, Thierry Denoeux:
A distributional framework for evaluation, comparison and uncertainty quantification in soft clustering. Int. J. Approx. Reason. 162: 109008 (2023) - [j119]Ling Huang, Su Ruan, Thierry Denoeux:
Semi-supervised multiple evidence fusion for brain tumor segmentation. Neurocomputing 535: 40-52 (2023) - [j118]Ling Huang, Su Ruan, Thierry Denoeux:
Application of belief functions to medical image segmentation: A review. Inf. Fusion 91: 737-756 (2023) - [j117]Andrea Campagner, Davide Ciucci, Thierry Denoeux:
A general framework for evaluating and comparing soft clusterings. Inf. Sci. 623: 70-93 (2023) - [j116]Thierry Denoeux:
Quantifying Prediction Uncertainty in Regression Using Random Fuzzy Sets: The ENNreg Model. IEEE Trans. Fuzzy Syst. 31(10): 3690-3699 (2023) - [c105]Thierry Denoeux:
Belief Functions on the Real Line Defined by Transformed Gaussian Random Fuzzy Numbers. FUZZ 2023: 1-6 - [i22]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation. CoRR abs/2309.05919 (2023) - 2022
- [j115]Lianmeng Jiao, Thierry Denoeux, Zhun-ga Liu, Quan Pan:
EGMM: An evidential version of the Gaussian mixture model for clustering. Appl. Soft Comput. 129: 109619 (2022) - [j114]John C. Aldrich, A. Philip Dawid, Thierry Denoeux, Prakash P. Shenoy, Vladimir Vovk:
Probability and statistics: Foundations and history. Special Issue in honor of Glenn Shafer. Int. J. Approx. Reason. 141: 1-4 (2022) - [j113]John C. Aldrich, A. Philip Dawid, Thierry Denoeux, Prakash P. Shenoy, Vladimir Vovk:
Glenn Shafer - A short biography. Int. J. Approx. Reason. 141: 5-10 (2022) - [j112]Andrea Campagner, Davide Ciucci, Thierry Denoeux:
Belief functions and rough sets: Survey and new insights. Int. J. Approx. Reason. 143: 192-215 (2022) - [j111]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Lymphoma segmentation from 3D PET-CT images using a deep evidential network. Int. J. Approx. Reason. 149: 39-60 (2022) - [c104]Wei Liu, Xiaodong Yue, Yufei Chen, Thierry Denoeux:
Trusted Multi-View Deep Learning with Opinion Aggregation. AAAI 2022: 7585-7593 - [c103]Andrea Campagner, Davide Ciucci, Thierry Denoeux:
A Distributional Approach for Soft Clustering Comparison and Evaluation. BELIEF 2022: 3-12 - [c102]Thierry Denoeux:
An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers. BELIEF 2022: 57-66 - [c101]Haijie Fu, Xiaodong Yue, Wei Liu, Thierry Denoeux:
Stable Clustering Ensemble Based on Evidence Theory. ICIP 2022: 2046-2050 - [c100]Ling Huang, Thierry Denoeux, Pierre Vera, Su Ruan:
Evidence Fusion with Contextual Discounting for Multi-modality Medical Image Segmentation. MICCAI (5) 2022: 401-411 - [i21]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Lymphoma segmentation from 3D PET-CT images using a deep evidential network. CoRR abs/2201.13078 (2022) - [i20]Thierry Denoeux:
Reasoning with fuzzy and uncertain evidence using epistemic random fuzzy sets: general framework and practical models. CoRR abs/2202.08081 (2022) - [i19]Andrea Campagner, Davide Ciucci, Thierry Denoeux:
A Distributional Approach for Soft Clustering Comparison and Evaluation. CoRR abs/2206.09827 (2022) - [i18]Ling Huang, Thierry Denoeux, Pierre Vera, Su Ruan:
Evidence fusion with contextual discounting for multi-modality medical image segmentation. CoRR abs/2206.11739 (2022) - [i17]Thierry Denoeux:
An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers. CoRR abs/2208.00647 (2022) - 2021
- [j110]Zheng Tong, Philippe Xu, Thierry Denoeux:
Evidential fully convolutional network for semantic segmentation. Appl. Intell. 51(9): 6376-6399 (2021) - [j109]Thierry Denoeux:
Belief functions induced by random fuzzy sets: A general framework for representing uncertain and fuzzy evidence. Fuzzy Sets Syst. 424: 63-91 (2021) - [j108]Jianxu Liu, Songsak Sriboonchitta, Aree Wiboonpongse, Thierry Denoeux:
A trivariate Gaussian copula stochastic frontier model with sample selection. Int. J. Approx. Reason. 137: 181-198 (2021) - [j107]Zheng Tong, Philippe Xu, Thierry Denoeux:
An evidential classifier based on Dempster-Shafer theory and deep learning. Neurocomputing 450: 275-293 (2021) - [j106]Thierry Denoeux:
Distributed combination of belief functions. Inf. Fusion 65: 179-191 (2021) - [j105]Thierry Denoeux:
NN-EVCLUS: Neural network-based evidential clustering. Inf. Sci. 572: 297-330 (2021) - [j104]Liyao Ma, Thierry Denoeux:
Partial classification in the belief function framework. Knowl. Based Syst. 214: 106742 (2021) - [j103]Minh-Quyet Ha, Duong-Nguyen Nguyen, Viet Cuong Nguyen, Takahiro Nagata, Toyohiro Chikyow, Hiori Kino, Takashi Miyake, Thierry Denoeux, Van-Nam Huynh, Hieu-Chi Dam:
Evidence-based recommender system for high-entropy alloys. Nat. Comput. Sci. 1(7): 470-478 (2021) - [j102]Zhi-gang Su, Qinghua Hu, Thierry Denoeux:
A Distributed Rough Evidential K-NN Classifier: Integrating Feature Reduction and Classification. IEEE Trans. Fuzzy Syst. 29(8): 2322-2335 (2021) - [j101]Zhunga Liu, Linqing Huang, Kuang Zhou, Thierry Denoeux:
Combination of Transferable Classification With Multisource Domain Adaptation Based on Evidential Reasoning. IEEE Trans. Neural Networks Learn. Syst. 32(5): 2015-2029 (2021) - [c99]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Evidential Segmentation of 3D PET/CT Images. BELIEF 2021: 159-167 - [c98]Zheng Tong, Philippe Xu, Thierry Denoeux:
Fusion of Evidential CNN Classifiers for Image Classification. BELIEF 2021: 168-176 - [c97]Ling Huang, Su Ruan, Thierry Denoeux:
Covid-19 Classification with Deep Neural Network and Belief Functions. BIBE 2021: 3:1-3:4 - [c96]Xiaoqian Zhou, Xiaodong Yue, Zhikang Xu, Thierry Denoeux, Yufei Chen:
Deep Neural Networks with Prior Evidence for Bladder Cancer Staging. BIBM 2021: 1221-1226 - [c95]Ling Huang, Su Ruan, Thierry Denoeux:
Belief Function-Based Semi-Supervised Learning For Brain Tumor Segmentation. ISBI 2021: 160-164 - [c94]Ling Huang, Thierry Denoeux, David Tonnelet, Pierre Decazes, Su Ruan:
Deep PET/CT Fusion with Dempster-Shafer Theory for Lymphoma Segmentation. MLMI@MICCAI 2021: 30-39 - [e10]Thierry Denoeux, Eric Lefèvre, Zhunga Liu, Frédéric Pichon:
Belief Functions: Theory and Applications - 6th International Conference, BELIEF 2021, Shanghai, China, October 15-19, 2021, Proceedings. Lecture Notes in Computer Science 12915, Springer 2021, ISBN 978-3-030-88600-4 [contents] - [i16]Ling Huang, Su Ruan, Thierry Denoeux:
Covid-19 classification with deep neural network and belief functions. CoRR abs/2101.06958 (2021) - [i15]Ling Huang, Su Ruan, Thierry Denoeux:
Belief function-based semi-supervised learning for brain tumor segmentation. CoRR abs/2102.00097 (2021) - [i14]Zheng Tong, Philippe Xu, Thierry Denoeux:
Evidential fully convolutional network for semantic segmentation. CoRR abs/2103.13544 (2021) - [i13]Zheng Tong, Philippe Xu, Thierry Denoeux:
An evidential classifier based on Dempster-Shafer theory and deep learning. CoRR abs/2103.13549 (2021) - [i12]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Evidential segmentation of 3D PET/CT images. CoRR abs/2104.13293 (2021) - [i11]Ling Huang, Thierry Denoeux, David Tonnelet, Pierre Decazes, Su Ruan:
Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation. CoRR abs/2108.05422 (2021) - [i10]Zheng Tong, Philippe Xu, Thierry Denoeux:
Fusion of evidential CNN classifiers for image classification. CoRR abs/2108.10233 (2021) - [i9]Emmanuel Ramasso, Thierry Denoeux, Gaël Chevallier:
Clustering acoustic emission data streams with sequentially appearing clusters using mixture models. CoRR abs/2108.11211 (2021) - 2020
- [j100]Feng Li, Shoumei Li, Thierry Denoeux:
Combining clusterings in the belief function framework. Array 6: 100018 (2020) - [j99]Thierry Denoeux, Prakash P. Shenoy:
An interval-valued utility theory for decision making with Dempster-Shafer belief functions. Int. J. Approx. Reason. 124: 194-216 (2020) - [j98]Thierry Denoeux:
Calibrated model-based evidential clustering using bootstrapping. Inf. Sci. 528: 17-45 (2020) - [c93]Bin Yuan, Xiaodong Yue, Ying Lv, Thierry Denoeux:
Evidential Deep Neural Networks for Uncertain Data Classification. KSEM (2) 2020: 427-437 - [p7]Thierry Denoeux, Didier Dubois, Henri Prade:
Representations of Uncertainty in Artificial Intelligence: Probability and Possibility. A Guided Tour of Artificial Intelligence Research (1) (I) 2020: 69-117 - [p6]Thierry Denoeux, Didier Dubois, Henri Prade:
Representations of Uncertainty in AI: Beyond Probability and Possibility. A Guided Tour of Artificial Intelligence Research (1) (I) 2020: 119-150 - [i8]Thierry Denoeux:
Belief functions induced by random fuzzy sets: Application to statistical inference. CoRR abs/2004.11638 (2020) - [i7]Thierry Denoeux:
NN-EVCLUS: Neural Network-based Evidential Clustering. CoRR abs/2009.12795 (2020) - [i6]Lianmeng Jiao, Thierry Denoeux, Zhun-ga Liu, Quan Pan:
EGMM: an Evidential Version of the Gaussian Mixture Model for Clustering. CoRR abs/2010.01333 (2020)
2010 – 2019
- 2019
- [j97]Thierry Denoeux:
Editorial: Opening up computer science. Array 1-2: 100005 (2019) - [j96]Thierry Denoeux:
Decision-making with belief functions: A review. Int. J. Approx. Reason. 109: 87-110 (2019) - [j95]Thierry Denoeux, Orakanya Kanjanatarakul, Songsak Sriboonchitta:
A new evidential K-nearest neighbor rule based on contextual discounting with partially supervised learning. Int. J. Approx. Reason. 113: 287-302 (2019) - [j94]Thierry Denoeux:
Logistic regression, neural networks and Dempster-Shafer theory: A new perspective. Knowl. Based Syst. 176: 54-67 (2019) - [j93]Zhi-gang Su, Thierry Denoeux:
BPEC: Belief-Peaks Evidential Clustering. IEEE Trans. Fuzzy Syst. 27(1): 111-123 (2019) - [j92]Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, Pierre Vera:
Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions. IEEE Trans. Image Process. 28(2): 755-766 (2019) - [c92]Thierry Denoeux, Orakanya Kanjanatarakul:
Multistep Prediction using Point-Cloud Approximation of Continuous Belief Functions. FUZZ-IEEE 2019: 1-6 - [c91]Yixuan Qiao, Shoumei Li, Thierry Denoeux:
Collaborative Evidential Clustering. IFSA/NAFIPS 2019: 518-530 - [c90]Thierry Denoeux, Prakash P. Shenoy:
An Axiomatic Utility Theory for Dempster-Shafer Belief Functions. ISIPTA 2019: 145-155 - [c89]Liyao Ma, Thierry Denoeux:
Making Set-Valued Predictions in Evidential Classification: A Comparison of Different Approaches. ISIPTA 2019: 276-285 - [c88]Zheng Tong, Philippe Xu, Thierry Denoeux:
ConvNet and Dempster-Shafer Theory for Object Recognition. SUM 2019: 368-381 - [p5]Frédéric Pichon, Didier Dubois, Thierry Denoeux:
Quality of Information Sources in Information Fusion. Information Quality in Information Fusion and Decision Making 2019: 31-49 - [e9]Sébastien Destercke, Thierry Denoeux, María Angeles Gil, Przemyslaw Grzegorzewski, Olgierd Hryniewicz:
Uncertainty Modelling in Data Science, SMPS 2018, Compiègne, France, 17-21 September 2018. Advances in Intelligent Systems and Computing 832, Springer 2019, ISBN 978-3-319-97546-7 [contents] - [i5]Thierry Denoeux:
Calibrated model-based evidential clustering using bootstrapping. CoRR abs/1912.06137 (2019) - [i4]Thierry Denoeux, Prakash P. Shenoy:
An Interval-Valued Utility Theory for Decision Making with Dempster-Shafer Belief Functions. CoRR abs/1912.06594 (2019) - [i3]Zied Bouraoui, Antoine Cornuéjols, Thierry Denoeux, Sébastien Destercke, Didier Dubois, Romain Guillaume, João Marques-Silva, Jérôme Mengin, Henri Prade, Steven Schockaert, Mathieu Serrurier, Christel Vrain:
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group). CoRR abs/1912.06612 (2019) - 2018
- [j91]Thierry Denoeux, Shoumei Li:
Frequency-calibrated belief functions: Review and new insights. Int. J. Approx. Reason. 92: 232-254 (2018) - [j90]Liqi Sui, Pierre Feissel, Thierry Denoeux:
Identification of elastic properties in the belief function framework. Int. J. Approx. Reason. 101: 69-87 (2018) - [j89]Zhi-gang Su, Thierry Denoeux, Yong-sheng Hao, Ming Zhao:
Evidential K-NN classification with enhanced performance via optimizing a class of parametric conjunctive t-rules. Knowl. Based Syst. 142: 7-16 (2018) - [j88]Feng Li, Shoumei Li, Thierry Denoeux:
k-CEVCLUS: Constrained evidential clustering of large dissimilarity data. Knowl. Based Syst. 142: 29-44 (2018) - [j87]Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, Pierre Vera:
Spatial Evidential Clustering With Adaptive Distance Metric for Tumor Segmentation in FDG-PET Images. IEEE Trans. Biomed. Eng. 65(1): 21-30 (2018) - [j86]Thierry Denoeux, Shoumei Li, Songsak Sriboonchitta:
Evaluating and Comparing Soft Partitions: An Approach Based on Dempster-Shafer Theory. IEEE Trans. Fuzzy Syst. 26(3): 1231-1244 (2018) - [c87]Thierry Denoeux:
Logistic Regression Revisited: Belief Function Analysis. BELIEF 2018: 57-64 - [c86]Orakanya Kanjanatarakul, Siwarat Kuson, Thierry Denoeux:
An Evidential K-Nearest Neighbor Classifier Based on Contextual Discounting and Likelihood Maximization. BELIEF 2018: 155-162 - [p4]Thierry Denoeux:
Quantifying Predictive Uncertainty Using Belief Functions: Different Approaches and Practical Construction. Predictive Econometrics and Big Data 2018: 157-176 - [e8]Sébastien Destercke, Thierry Denoeux, Fabio Cuzzolin, Arnaud Martin:
Belief Functions: Theory and Applications - 5th International Conference, BELIEF 2018, Compiègne, France, September 17-21, 2018, Proceedings. Lecture Notes in Computer Science 11069, Springer 2018, ISBN 978-3-319-99382-9 [contents] - [e7]Van-Nam Huynh, Masahiro Inuiguchi, Dang Hung Tran, Thierry Denoeux:
Integrated Uncertainty in Knowledge Modelling and Decision Making - 6th International Symposium, IUKM 2018, Hanoi, Vietnam, March 15-17, 2018, Proceedings. Lecture Notes in Computer Science 10758, Springer 2018, ISBN 978-3-319-75428-4 [contents] - [i2]Thierry Denoeux:
Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspective. CoRR abs/1807.01846 (2018) - [i1]Thierry Denoeux:
Decision-Making with Belief Functions: a Review. CoRR abs/1808.05322 (2018) - 2017
- [j85]Benjamin Quost, Thierry Denoeux, Shoumei Li:
Parametric classification with soft labels using the evidential EM algorithm: linear discriminant analysis versus logistic regression. Adv. Data Anal. Classif. 11(4): 659-690 (2017) - [j84]Jean-Baptiste Bordes, Franck Davoine, Philippe Xu, Thierry Denoeux:
Evidential grammars: A compositional approach for scene understanding. Application to multimodal street data. Appl. Soft Comput. 61: 1173-1185 (2017) - [j83]Songsak Sriboonchitta, Jianxu Liu, Aree Wiboonpongse, Thierry Denoeux:
A double-copula stochastic frontier model with dependent error components and correction for sample selection. Int. J. Approx. Reason. 80: 174-184 (2017) - [c85]Feng Li, Shoumei Li, Nana Tang, Thierry Denoeux:
Constrained interval-valued linear regression model. FUSION 2017: 1-8 - [c84]Chunfeng Lian, Su Ruan, Thierry Denoeux, Yu Guo, Pierre Vera:
Accurate tumor segmentation in FDG-PET images with guidance of complementary CT images. ICIP 2017: 4447-4451 - [c83]Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, Pierre Vera:
Tumor delineation in FDG-PET images using a new evidential clustering algorithm with spatial regularization and adaptive distance metric. ISBI 2017: 1177-1180 - [c82]Orakanya Kanjanatarakul, Thierry Denoeux:
Distributed data fusion in the dempster-shafer framework. SoSE 2017: 1-6 - 2016
- [j82]Benjamin Quost, Thierry Denoeux:
Clustering and classification of fuzzy data using the fuzzy EM algorithm. Fuzzy Sets Syst. 286: 134-156 (2016) - [j81]Philippe Xu, Franck Davoine, Hongbin Zha, Thierry Denoeux:
Evidential calibration of binary SVM classifiers. Int. J. Approx. Reason. 72: 55-70 (2016) - [j80]Orakanya Kanjanatarakul, Thierry Denoeux, Songsak Sriboonchitta:
Prediction of future observations using belief functions: A likelihood-based approach. Int. J. Approx. Reason. 72: 71-94 (2016) - [j79]Thierry Denoeux:
40 years of Dempster-Shafer theory. Int. J. Approx. Reason. 79: 1-6 (2016) - [j78]Thierry Denoeux, Songsak Sriboonchitta, Orakanya Kanjanatarakul:
Evidential clustering of large dissimilarity data. Knowl. Based Syst. 106: 179-195 (2016) - [j77]Chunfeng Lian, Su Ruan, Thierry Denoeux, Fabrice Jardin, Pierre Vera:
Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction. Medical Image Anal. 32: 257-268 (2016) - [j76]Philippe Xu, Franck Davoine, Jean-Baptiste Bordes, Huijing Zhao, Thierry Denoeux:
Multimodal information fusion for urban scene understanding. Mach. Vis. Appl. 27(3): 331-349 (2016) - [j75]Sawsan Kanj, Fahed Abdallah, Thierry Denoeux, Kifah Tout:
Editing training data for multi-label classification with the k-nearest neighbor rule. Pattern Anal. Appl. 19(1): 145-161 (2016) - [j74]Marie-Hélène Masson, Sébastien Destercke, Thierry Denoeux:
Modelling and predicting partial orders from pairwise belief functions. Soft Comput. 20(3): 939-950 (2016) - [j73]Chunfeng Lian, Su Ruan, Thierry Denoeux:
Dissimilarity Metric Learning in the Belief Function Framework. IEEE Trans. Fuzzy Syst. 24(6): 1555-1564 (2016) - [j72]Lianmeng Jiao, Thierry Denoeux, Quan Pan:
A Hybrid Belief Rule-Based Classification System Based on Uncertain Training Data and Expert Knowledge. IEEE Trans. Syst. Man Cybern. Syst. 46(12): 1711-1723 (2016) - [c81]Orakanya Kanjanatarakul, Songsak Sriboonchitta, Thierry Denoeux:
k-EVCLUS: Clustering Large Dissimilarity Data in the Belief Function Framework. BELIEF 2016: 105-112 - [c80]Liqi Sui, Pierre Feissel, Thierry Denoeux:
Identification of Elastic Properties Based on Belief Function Inference. BELIEF 2016: 182-189 - [c79]Chunfeng Lian, Su Ruan, Thierry Denoeux:
Joint Feature Transformation and Selection Based on Dempster-Shafer Theory. IPMU (1) 2016: 253-261 - [c78]Thierry Denoeux, Orakanya Kanjanatarakul:
Evidential Clustering: A Review. IUKM 2016: 24-35 - [c77]Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, Pierre Vera:
Robust Cancer Treatment Outcome Prediction Dealing with Small-Sized and Imbalanced Data from FDG-PET Images. MICCAI (2) 2016: 61-69 - [c76]Thierry Denoeux, Orakanya Kanjanatarakul:
Beyond Fuzzy, Possibilistic and Rough: An Investigation of Belief Functions in Clustering. SMPS 2016: 157-164 - [e6]Van-Nam Huynh, Masahiro Inuiguchi, Bac Le, Bao Nguyen Le, Thierry Denoeux:
Integrated Uncertainty in Knowledge Modelling and Decision Making - 5th International Symposium, IUKM 2016, Da Nang, Vietnam, November 30 - December 2, 2016, Proceedings. Lecture Notes in Computer Science 9978, 2016, ISBN 978-3-319-49045-8 [contents] - 2015
- [j71]Aree Wiboonpongse, Jianxu Liu, Songsak Sriboonchitta, Thierry Denoeux:
Modeling dependence between error components of the stochastic frontier model using copula: Application to intercrop coffee production in Northern Thailand. Int. J. Approx. Reason. 65: 34-44 (2015) - [j70]Xun Wang, Shoumei Li, Thierry Denoeux:
Interval-Valued Linear Model. Int. J. Comput. Intell. Syst. 8(1): 114-127 (2015) - [j69]Lianmeng Jiao, Quan Pan, Thierry Denoeux, Yan Liang, Xiaoxue Feng:
Belief rule-based classification system: Extension of FRBCS in belief functions framework. Inf. Sci. 309: 26-49 (2015) - [j68]Thierry Denoeux, Orakanya Kanjanatarakul, Songsak Sriboonchitta:
EK-NNclus: A clustering procedure based on the evidential K-nearest neighbor rule. Knowl. Based Syst. 88: 57-69 (2015) - [j67]Chunfeng Lian, Su Ruan, Thierry Denoeux:
An evidential classifier based on feature selection and two-step classification strategy. Pattern Recognit. 48(7): 2318-2327 (2015) - [c75]Lianmeng Jiao, Thierry Denoeux, Quan Pan:
Evidential Editing K-Nearest Neighbor Classifier. ECSQARU 2015: 461-471 - [c74]Philippe Xu, Franck Davoine, Thierry Denoeux:
Evidential multinomial logistic regression for multiclass classifier calibration. FUSION 2015: 1106-1112 - [c73]Chunfeng Lian, Su Ruan, Thierry Denoeux, Pierre Vera:
Outcome prediction in tumour therapy based on Dempster-Shafer theory. ISBI 2015: 63-66 - [c72]Abdel-Djalil Ourabah, Benjamin Quost, Atef Gayed, Thierry Denoeux:
Estimating energy consumption of a PHEV using vehicle and on-board navigation data. Intelligent Vehicles Symposium 2015: 755-760 - [c71]Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, Pierre Vera:
Dempster-Shafer Theory Based Feature Selection with Sparse Constraint for Outcome Prediction in Cancer Therapy. MICCAI (3) 2015: 695-702 - [p3]Orakanya Kanjanatarakul, Nachatchapong Kaewsompong, Songsak Sriboonchitta, Thierry Denoeux:
Estimation and Prediction Using Belief Functions: Application to Stochastic Frontier Analysis. Econometrics of Risk 2015: 171-184 - [p2]Supanika Leurcharusmee, Jirakom Sirisrisakulchai, Songsak Sriboonchitta, Thierry Denoeux:
The Classifier Chain Generalized Maximum Entropy Model for Multi-label Choice Problems. Econometrics of Risk 2015: 185-199 - [e5]Sébastien Destercke, Thierry Denoeux:
Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, 2015. Proceedings. Lecture Notes in Computer Science 9161, Springer 2015, ISBN 978-3-319-20806-0 [contents] - [e4]Van-Nam Huynh, Masahiro Inuiguchi, Thierry Denoeux:
Integrated Uncertainty in Knowledge Modelling and Decision Making - 4th International Symposium, IUKM 2015, Nha Trang, Vietnam, October 15-17, 2015, Proceedings. Lecture Notes in Computer Science 9376, Springer 2015, ISBN 978-3-319-25134-9 [contents] - 2014
- [j66]Ana Colubi, Thierry Denoeux:
Special issue on imprecision in statistical data analysis. Comput. Stat. Data Anal. 71: 787-788 (2014) - [j65]Nadia Ben Abdallah, Nassima Mouhous Voyneau, Thierry Denoeux:
Combining statistical and expert evidence using belief functions: Application to centennial sea level estimation taking into account climate change. Int. J. Approx. Reason. 55(1): 341-354 (2014) - [j64]Orakanya Kanjanatarakul, Songsak Sriboonchitta, Thierry Denoeux:
Forecasting using belief functions: An application to marketing econometrics. Int. J. Approx. Reason. 55(5): 1113-1128 (2014) - [j63]Thierry Denoeux:
Likelihood-based belief function: Justification and some extensions to low-quality data. Int. J. Approx. Reason. 55(7): 1535-1547 (2014) - [j62]Thierry Denoeux:
Rejoinder on "Likelihood-based belief function: Justification and some extensions to low-quality data". Int. J. Approx. Reason. 55(7): 1614-1617 (2014) - [j61]Benoît Lelandais, Su Ruan, Thierry Denoeux, Pierre Vera, Isabelle Gardin:
Fusion of multi-tracer PET images for dose painting. Medical Image Anal. 18(7): 1247-1259 (2014) - [j60]Violaine Antoine, Benjamin Quost, Marie-Hélène Masson, Thierry Denoeux:
CEVCLUS: evidential clustering with instance-level constraints for relational data. Soft Comput. 18(7): 1321-1335 (2014) - [j59]Thierry Denoeux, Nicole El Zoghby, Véronique Cherfaoui, Antoine Jouglet:
Optimal Object Association in the Dempster-Shafer Framework. IEEE Trans. Cybern. 44(12): 2521-2531 (2014) - [j58]Philippe Xu, Franck Davoine, Jean-Baptiste Bordes, Thierry Denoeux:
Fusion d'informations pour la compréhension de scènes. Traitement du Signal 31(1-2): 57-80 (2014) - [j57]Emmanuel Ramasso, Thierry Denoeux:
Making Use of Partial Knowledge About Hidden States in HMMs: An Approach Based on Belief Functions. IEEE Trans. Fuzzy Syst. 22(2): 395-405 (2014) - [c70]Philippe Xu, Franck Davoine, Thierry Denoeux:
Evidential Logistic Regression for Binary SVM Classifier Calibration. Belief Functions 2014: 49-57 - [c69]Supanika Leurcharusmee, Peerapat Jatukannyaprateep, Songsak Sriboonchitta, Thierry Denoeux:
The Evidence-Theoretic k-NN Rule for Rank-Ordered Data: Application to Predict an Individual's Source of Loan. Belief Functions 2014: 58-67 - [c68]Nicolas Sutton-Charani, Sébastien Destercke, Thierry Denoeux:
Training and Evaluating Classifiers from Evidential Data: Application to E 2 M Decision Tree Pruning. Belief Functions 2014: 87-94 - [c67]Kittawit Autchariyapanitkul, Somsak Chanaim, Songsak Sriboonchitta, Thierry Denoeux:
Predicting Stock Returns in the Capital Asset Pricing Model Using Quantile Regression and Belief Functions. Belief Functions 2014: 219-226 - [c66]Philippe Xu, Franck Davoine, Thierry Denoeux:
Evidential combination of pedestrian detectors. BMVC 2014 - [c65]Lianmeng Jiao, Thierry Denoeux, Quan Pan:
Fusion of pairwise nearest-neighbor classifiers based on pairwise-weighted distance metric and Dempster-Shafer theory. FUSION 2014: 1-7 - [c64]Nicolas Sutton-Charani, Sébastien Destercke, Thierry Denoeux:
Application of E 2 M Decision Trees to Rubber Quality Prediction. IPMU (1) 2014: 107-116 - [c63]Nicole El Zoghby, Véronique Cherfaoui, Thierry Denoeux:
Evidential distributed dynamic map for cooperative perception in VANets. Intelligent Vehicles Symposium 2014: 1421-1426 - [e3]Van-Nam Huynh, Thierry Denoeux, Dang Hung Tran, Anh-Cuong Le, Son Bao Pham:
Knowledge and Systems Engineering - Proceedings of the Fifth International Conference, KSE 2013, Volume 1, Hanoi, Vietnam, 17-19 October, 2013. Advances in Intelligent Systems and Computing 244, Springer 2014, ISBN 978-3-319-02740-1 [contents] - [e2]Van-Nam Huynh, Thierry Denoeux, Dang Hung Tran, Anh-Cuong Le, Son Bao Pham:
Knowledge and Systems Engineering - Proceedings of the Fifth International Conference KSE 2013, Volume 2. Advances in Intelligent Systems and Computing 245, Springer 2014, ISBN 978-3-319-02820-0 [contents] - 2013
- [j56]Thierry Denoeux:
Maximum Likelihood Estimation from Uncertain Data in the Belief Function Framework. IEEE Trans. Knowl. Data Eng. 25(1): 119-130 (2013) - [c62]Nicole El Zoghby, Véronique Cherfaoui, Thierry Denoeux:
Optimal object association from pairwise evidential mass functions. FUSION 2013: 774-780 - [c61]Nadia Ben Abdallah, Nassima Mouhous Voyneau, Thierry Denoeux:
Using Dempster-Shafer theory to model uncertainty in climate change and environmental impact assessments. FUSION 2013: 2117-2124 - [c60]Nicolas Sutton-Charani, Sébastien Destercke, Thierry Denoeux:
Learning Decision Trees from Uncertain Data with an Evidential EM Approach. ICMLA (1) 2013: 111-116 - [c59]Jean-Baptiste Bordes, Franck Davoine, Philippe Xu, Thierry Denoeux:
Evidential Grammars for Image Interpretation - Application to Multimodal Traffic Scene Understanding. IUKM 2013: 65-78 - [c58]Philippe Xu, Franck Davoine, Jean-Baptiste Bordes, Huijing Zhao, Thierry Denoeux:
Information Fusion on Oversegmented Images: An Application for Urban Scene Understanding. MVA 2013: 189-193 - 2012
- [j55]Thierry Denoeux, Marie-Hélène Masson:
Evidential reasoning in large partially ordered sets - Application to multi-label classification, ensemble clustering and preference aggregation. Ann. Oper. Res. 195(1): 135-161 (2012) - [j54]Violaine Antoine, Benjamin Quost, Marie-Hélène Masson, Thierry Denoeux:
CECM: Constrained evidential C-means algorithm. Comput. Stat. Data Anal. 56(4): 894-914 (2012) - [j53]Frédéric Pichon, Didier Dubois, Thierry Denoeux:
Relevance and truthfulness in information correction and fusion. Int. J. Approx. Reason. 53(2): 159-175 (2012) - [j52]Etienne Côme, Latifa Oukhellou, Thierry Denoeux, Patrice Aknin:
Fault diagnosis of a railway device using semi-supervised independent factor analysis with mixing constraints. Pattern Anal. Appl. 15(3): 313-326 (2012) - [j51]Zohra Leila Cherfi, Latifa Oukhellou, Etienne Côme, Thierry Denoeux, Patrice Aknin:
Partially supervised Independent Factor Analysis using soft labels elicited from multiple experts: application to railway track circuit diagnosis. Soft Comput. 16(5): 741-754 (2012) - [c57]Sawsan Kanj, Fahed Abdallah, Thierry Denoeux:
Evidential Multi-label Classification Using the Random k-Label Sets Approach. Belief Functions 2012: 21-28 - [c56]Nicolas Sutton-Charani, Sébastien Destercke, Thierry Denoeux:
Classification Trees Based on Belief Functions. Belief Functions 2012: 77-84 - [c55]Marie-Hélène Masson, Thierry Denoeux:
Ranking from Pairwise Comparisons in the Belief Functions Framework. Belief Functions 2012: 311-318 - [c54]Nicole El Zoghby, Véronique Cherfaoui, Bertrand Ducourthial, Thierry Denoeux:
Distributed Data Fusion for Detecting Sybil Attacks in VANETs. Belief Functions 2012: 351-358 - [c53]Emmanuel Ramasso, Thierry Denoeux, Noureddine Zerhouni:
Partially-Hidden Markov Models. Belief Functions 2012: 359-366 - [c52]Didier Dubois, Thierry Denoeux:
Conditioning in Dempster-Shafer Theory: Prediction vs. Revision. Belief Functions 2012: 385-392 - [c51]Nadia Ben Abdallah, Nassima Mouhous Voyneau, Thierry Denoeux:
Combining Statistical and Expert Evidence within the D-S Framework: Application to Hydrological Return Level Estimation. Belief Functions 2012: 393-400 - [c50]Sawsan Kanj, Fahed Abdallah, Thierry Denoeux:
Purifying training data to improve performance of multi-label classification algorithms. FUSION 2012: 1784-1791 - [c49]Rui Jorge Almeida, Thierry Denoeux, Uzay Kaymak:
Constructing Rule-Based Models Using the Belief Functions Framework. IPMU (3) 2012: 554-563 - [c48]Bertrand Ducourthial, Véronique Cherfaoui, Thierry Denoeux:
Self-stabilizing Distributed Data Fusion. SSS 2012: 148-162 - [e1]Thierry Denoeux, Marie-Hélène Masson:
Belief Functions: Theory and Applications - Proceedings of the 2nd International Conference on Belief Functions, Compiègne, France, 9-11 May 2012. Advances in Intelligent and Soft Computing 164, Springer 2012, ISBN 978-3-642-29460-0 [contents] - 2011
- [j50]Zoulficar Younes, Fahed Abdallah, Thierry Denoeux, Hichem Snoussi:
A Dependent Multilabel Classification Method Derived from the k-Nearest Neighbor Rule. EURASIP J. Adv. Signal Process. 2011 (2011) - [j49]Thierry Denoeux:
Maximum likelihood estimation from fuzzy data using the EM algorithm. Fuzzy Sets Syst. 183(1): 72-91 (2011) - [j48]Marie-Hélène Masson, Thierry Denoeux:
Ensemble clustering in the belief functions framework. Int. J. Approx. Reason. 52(1): 92-109 (2011) - [j47]Benjamin Quost, Marie-Hélène Masson, Thierry Denoeux:
Classifier fusion in the Dempster-Shafer framework using optimized t-norm based combination rules. Int. J. Approx. Reason. 52(3): 353-374 (2011) - [j46]Fahed Abdallah, Ghalia Nassreddine, Thierry Denoeux:
A Multiple-Hypothesis Map-Matching Method Suitable for Weighted and Box-Shaped State Estimation for Localization. IEEE Trans. Intell. Transp. Syst. 12(4): 1495-1510 (2011) - [c47]Violaine Antoine, Benjamin Quost, Mylène Masson, Thierry Denoeux:
CEVCLUS: Constrained evidential clustering of proximity data. EUSFLAT Conf. 2011: 876-882 - [c46]Latifa Oukhellou, Etienne Côme, Patrice Aknin, Thierry Denoeux:
Semi-supervised Feature Extraction Using Independent Factor Analysis. ICMLA (2) 2011: 330-333 - [c45]Zohra Leila Cherfi, Latifa Oukhellou, Etienne Côme, Thierry Denoeux, Patrice Aknin:
Using Imprecise and Uncertain Information to Enhance the Diagnosis of a Railway Device. NL-MUA 2011: 213-220 - 2010
- [j45]Thierry Denoeux, Zoulficar Younes, Fahed Abdallah:
Representing uncertainty on set-valued variables using belief functions. Artif. Intell. 174(7-8): 479-499 (2010) - [j44]Latifa Oukhellou, Alexandra Debiolles, Thierry Denoeux, Patrice Aknin:
Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion. Eng. Appl. Artif. Intell. 23(1): 117-128 (2010) - [j43]Frédéric Pichon, Thierry Denoeux:
The Unnormalized Dempster's Rule of Combination: A New Justification from the Least Commitment Principle and Some Extensions. J. Autom. Reason. 45(1): 61-87 (2010) - [j42]Ghalia Nassreddine, Fahed Abdallah, Thierry Denoeux:
State Estimation Using Interval Analysis and Belief-Function Theory: Application to Dynamic Vehicle Localization. IEEE Trans. Syst. Man Cybern. Part B 40(5): 1205-1218 (2010) - [c44]Violaine Antoine, Benjamin Quost, Marie-Hélène Masson, Thierry Denoeux:
CECM: Adding pairwise constraints to evidential clustering. FUZZ-IEEE 2010: 1-8 - [c43]Zoulficar Younes, Fahed Abdallah, Thierry Denoeux:
Fuzzy multi-label learning under veristic variables. FUZZ-IEEE 2010: 1-8 - [c42]Zoulficar Younes, Fahed Abdallah, Thierry Denoeux:
Evidential Multi-Label Classification Approach to Learning from Data with Imprecise Labels. IPMU 2010: 119-128 - [c41]Thierry Denoeux, Marie-Hélène Masson:
Dempster-Shafer Reasoning in Large Partially Ordered Sets: Applications in Machine Learning. IUM 2010: 39-54 - [c40]Thierry Denoeux:
Theory of Belief Functions for Data Analysis and Machine Learning Applications: Review and Prospects. KSEM 2010: 3 - [c39]Thierry Denoeux:
Maximum Likelihood from Evidential Data: An Extension of the EM Algorithm. SMPS 2010: 181-188 - [c38]Didier Dubois, Thierry Denoeux:
Statistical Inference with Belief Functions and Possibility Measures: A Discussion of Basic Assumptions. SMPS 2010: 217-225 - [c37]Benjamin Quost, Thierry Denoeux:
Clustering Fuzzy Data Using the Fuzzy EM Algorithm. SUM 2010: 333-346
2000 – 2009
- 2009
- [j41]David Mercier, Genevieve Cron, Thierry Denoeux, Marie-Hélène Masson:
Decision fusion for postal address recognition using belief functions. Expert Syst. Appl. 36(3): 5643-5653 (2009) - [j40]Thierry Denoeux:
Extending stochastic ordering to belief functions on the real line. Inf. Sci. 179(9): 1362-1376 (2009) - [j39]Etienne Côme, Latifa Oukhellou, Thierry Denoeux, Patrice Aknin:
Learning from partially supervised data using mixture models and belief functions. Pattern Recognit. 42(3): 334-348 (2009) - [j38]Marie-Hélène Masson, Thierry Denoeux:
RECM: Relational evidential c-means algorithm. Pattern Recognit. Lett. 30(11): 1015-1026 (2009) - [c36]Marie-Hélène Masson, Thierry Denoeux:
Belief Functions and Cluster Ensembles. ECSQARU 2009: 323-334 - [c35]Etienne Côme, Latifa Oukhellou, Patrice Aknin, Thierry Denoeux:
Partially-supervised learning in Independent Factor Analysis. ESANN 2009 - [c34]Krystyna Biletska, Sophie Midenet, Marie-Hélène Masson, Thierry Denoeux:
Fuzzy Modelling of Sensor Data for the Estimation of an Origin-Destination Matrix. IFSA/EUSFLAT Conf. 2009: 849-854 - [c33]Ghalia Nassreddine, Fahed Abdallah, Thierry Denoeux:
A state estimation method for multiple model systems using belief function theory. FUSION 2009: 506-513 - [c32]Etienne Côme, Latifa Oukhellou, Thierry Denoeux, Patrice Aknin:
Noiseless Independent Factor Analysis with Mixing Constraints in a Semi-supervised Framework. Application to Railway Device Fault Diagnosis. ICANN (2) 2009: 416-425 - [c31]Krystyna Biletska, Marie-Hélène Masson, Sophie Midenet, Thierry Denoeux:
Short-time OD matrix estimation for a complex junction using fuzzy-timed high-level petri nets. ITSC 2009: 1-6 - [c30]Benjamin Quost, Thierry Denoeux:
Learning from data with uncertain labels by boosting credal classifiers. KDD Workshop on Knowledge Discovery from Uncertain Data 2009: 38-47 - [c29]Krystyna Biletska, Marie-Hélène Masson, Sophie Midenet, Thierry Denoeux:
Multisensor data fusion for OD matrix estimation. SMC 2009: 3024-3029 - [c28]Zoulficar Younes, Fahed Abdallah, Thierry Denoeux:
An Evidence-Theoretic k-Nearest Neighbor Rule for Multi-label Classification. SUM 2009: 297-308 - 2008
- [j37]Thierry Denoeux:
Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence. Artif. Intell. 172(2-3): 234-264 (2008) - [j36]Thierry Denoeux:
Special issue in memory of Philippe Smets (1938-2005). Int. J. Approx. Reason. 48(2): 349-351 (2008) - [j35]Astride Aregui, Thierry Denoeux:
Constructing consonant belief functions from sample data using confidence sets of pignistic probabilities. Int. J. Approx. Reason. 49(3): 575-594 (2008) - [j34]David Mercier, Benjamin Quost, Thierry Denoeux:
Refined modeling of sensor reliability in the belief function framework using contextual discounting. Inf. Fusion 9(2): 246-258 (2008) - [j33]Marie-Hélène Masson, Thierry Denoeux:
ECM: An evidential version of the fuzzy c. Pattern Recognit. 41(4): 1384-1397 (2008) - [c27]Zoulficar Younes, Fahed Abdallah, Thierry Denoeux:
Multi-label classification algorithm derived from K-nearest neighbor rule with label dependencies. EUSIPCO 2008: 1-5 - [c26]Frédéric Pichon, Thierry Denoeux:
A New Justification of the Unnormalized Dempster's Rule of Combination from the Least Commitment Principle. FLAIRS 2008: 666-671 - [c25]Véronique Cherfaoui, Thierry Denoeux, Zohra Leila Cherfi:
Distributed data fusion: application to confidence management in vehicular networks. FUSION 2008: 1-8 - [c24]Ghalia Nassreddine, Fahed Abdallah, Thierry Denoeux:
Map matching algorithm using belief function theory. FUSION 2008: 1-8 - [c23]Benjamin Quost, Marie-Hélène Masson, Thierry Denoeux:
Refined classifier combination using belief functions. FUSION 2008: 1-7 - [c22]Etienne Côme, Latifa Oukhellou, Thierry Denoeux, Patrice Aknin:
Mixture Model Estimation with Soft Labels. SMPS 2008: 165-174 - [p1]Thierry Denoeux:
A k -Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory. Classic Works of the Dempster-Shafer Theory of Belief Functions 2008: 737-760 - 2007
- [j32]Benjamin Quost, Thierry Denoeux, Marie-Hélène Masson:
Pairwise classifier combination using belief functions. Pattern Recognit. Lett. 28(5): 644-653 (2007) - [c21]Thierry Denoeux:
Pattern Recognition and Information Fusion Using Belief Functions: Some Recent Developments. ECSQARU 2007: 1 - [c20]Astride Aregui, Thierry Denoeux:
Consonant Belief Function Induced by a Confidence Set of Pignistic Probabilities. ECSQARU 2007: 344-355 - [c19]Frédéric Pichon, Thierry Denoeux:
On Latent Belief Structures. ECSQARU 2007: 368-380 - [c18]Astride Aregui, Thierry Denoeux:
Fusion of one-class classifiers in the belief function framework. FUSION 2007: 1-8 - 2006
- [j31]Pierre-Alexandre Hébert, Marie-Hélène Masson, Thierry Denoeux:
Fuzzy multidimensional scaling. Comput. Stat. Data Anal. 51(1): 335-359 (2006) - [j30]Marie-Hélène Masson, Thierry Denoeux:
Inferring a possibility distribution from empirical data. Fuzzy Sets Syst. 157(3): 319-340 (2006) - [j29]Hugues Bersini, Thierry Denoeux, Didier Dubois, Henri Prade:
In Memoriam: Philippe Smets (1938-2005). Fuzzy Sets Syst. 157(8) (2006) - [j28]Hugues Bersini, Thierry Denoeux, Didier Dubois, Henri Prade:
Philippe Smets (1938-2005). Int. J. Approx. Reason. 41(3) (2006) - [j27]Thierry Denoeux:
Constructing belief functions from sample data using multinomial confidence regions. Int. J. Approx. Reason. 42(3): 228-252 (2006) - [j26]Sabrina Démotier, Paul Walter Schön, Thierry Denoeux:
Risk assessment based on weak information using belief functions: a case study in water treatment. IEEE Trans. Syst. Man Cybern. Syst. 36(3): 382-396 (2006) - [j25]Thierry Denoeux, Philippe Smets:
Classification Using Belief Functions: Relationship Between Case-Based and Model-Based Approaches. IEEE Trans. Syst. Man Cybern. Part B 36(6): 1395-1406 (2006) - [c17]Alexandra Debiolles, Latifa Oukhellou, Thierry Denoeux, Patrice Aknin:
Output coding of spatially dependent subclassifiers in evidential framework. Application to the diagnosis of railway track/vehicle transmission system. FUSION 2006: 1-6 - [c16]Thierry Denoeux:
The cautious rule of combination for belief functions and some extensions. FUSION 2006: 1-8 - [c15]David Mercier, Thierry Denoeux, Marie-Hélène Masson:
General Correction Mechanisms for Weakening or Reinforcing Belief Functions. FUSION 2006: 1-7 - 2005
- [j24]Thierry Denoeux:
R. P. Srivastava and T. J. Mock, Belief Functions in Business Decisions, in Studies in Fuzziness and Soft Computing, vol. 88, Physica-Verlag, Heidelberg (2002) ISBN 3-7908-1451-2 (345pp.). Fuzzy Sets Syst. 151(2): 435-436 (2005) - [j23]Thierry Denoeux, Marie-Hélène Masson, Pierre-Alexandre Hébert:
Nonparametric rank-based statistics and significance tests for fuzzy data. Fuzzy Sets Syst. 153(1): 1-28 (2005) - [j22]Thierry Denoeux, Piero P. Bonissone:
Editorial. Int. J. Approx. Reason. 40(3): 125-126 (2005) - [c14]David Mercier, Benjamin Quost, Thierry Denoeux:
Contextual Discounting of Belief Functions. ECSQARU 2005: 552-562 - 2004
- [j21]Simon Petit-Renaud, Thierry Denoeux:
Nonparametric regression analysis of uncertain and imprecise data using belief functions. Int. J. Approx. Reason. 35(1): 1-28 (2004) - [j20]Marie-Hélène Masson, Thierry Denoeux:
Clustering interval-valued proximity data using belief functions. Pattern Recognit. Lett. 25(2): 163-171 (2004) - [j19]Thierry Denoeux, Marie-Hélène Masson:
Principal component analysis of fuzzy data using autoassociative neural networks. IEEE Trans. Fuzzy Syst. 12(3): 336-349 (2004) - [j18]Thierry Denoeux, Marie-Hélène Masson:
EVCLUS: evidential clustering of proximity data. IEEE Trans. Syst. Man Cybern. Part B 34(1): 95-109 (2004) - [c13]Sandro Glaucio Maquiné de Souza, Thierry Denoeux, Yves Grandvalet:
Recycling experiments for sludge monitoring in waste water treatment. SMC (2) 2004: 1342-1347 - 2003
- [j17]Jérémie François, Yves Grandvalet, Thierry Denoeux, Jean-Michel Roger:
Resample and combine: an approach to improving uncertainty representation in evidential pattern classification. Inf. Fusion 4(2): 75-85 (2003) - [j16]Jérémie François, Yves Grandvalet, Thierry Denoeux, Jean-Michel Roger:
Addendum to resample and combine: an approach to improving uncertainty representation in evidential pattern classification. Inf. Fusion 4(3): 235-236 (2003) - [c12]Sabrina Démotier, Thierry Denoeux, Paul Walter Schön:
Risk Assessment in Drinking Water Production Using Belief Functions. ECSQARU 2003: 319-331 - [c11]Sabrina Démotier, Paul Walter Schön, Thierry Denoeux, Khaled Odeh:
A new approach to assess risk in water treatment using the belief function framework. SMC 2003: 1792-1797 - 2002
- [j15]Marie-Hélène Masson, Thierry Denoeux:
Multidimensional scaling of fuzzy dissimilarity data. Fuzzy Sets Syst. 128(3): 339-352 (2002) - [j14]Thierry Denoeux, Amel Ben Yaghlane:
Approximating the combination of belief functions using the fast Mo"bius transform in a coarsened frame. Int. J. Approx. Reason. 31(1-2): 77-101 (2002) - 2001
- [j13]Thierry Denoeux, Lalla Merieme Zouhal:
Handling possibilistic labels in pattern classification using evidential reasoning. Fuzzy Sets Syst. 122(3): 409-424 (2001) - [j12]Nicolas Valentin, Thierry Denoeux:
A neural network-based software sensor for coagulation control in a water treatment plant. Intell. Data Anal. 5(1): 23-39 (2001) - [j11]Thierry Denoeux:
Inner and Outer Approximation of Belief Structures Using a Hierarchical Clustering Approach. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 9(4): 437-460 (2001) - [c10]Amel Ben Yaghlane, Thierry Denoeux, Khaled Mellouli:
Coarsening Approximations of Belief Functions. ECSQARU 2001: 362-373 - [c9]Patrick Vannoorenberghe, Thierry Denoeux:
Likelihood-based Vs Distance-based Evidential Classifiers. FUZZ-IEEE 2001: 320-323 - 2000
- [j10]Thierry Denoeux:
Modeling vague beliefs using fuzzy-valued belief structures. Fuzzy Sets Syst. 116(2): 167-199 (2000) - [j9]Thierry Denoeux, Marie-Hélène Masson:
Multidimensional scaling of interval-valued dissimilarity data. Pattern Recognit. Lett. 21(1): 83-92 (2000) - [j8]Thierry Denoeux:
A neural network classifier based on Dempster-Shafer theory. IEEE Trans. Syst. Man Cybern. Part A 30(2): 131-150 (2000) - [c8]Thierry Denoeux, M. Skarstein Bjanger:
Induction of decision trees from partially classified data using belief functions. SMC 2000: 2923-2928
1990 – 1999
- 1999
- [j7]Thierry Denoeux:
Reasoning with imprecise belief structures. Int. J. Approx. Reason. 20(1): 79-111 (1999) - [c7]Simon Petit-Renaud, Thierry Denoeux:
Handling Different Forms of Uncertainty in Regression Analysis: A Fuzzy Belief Structure Approach. ESCQARU 1999: 340-351 - [c6]Michèle Rombaut, Iman Jarkass, Thierry Denoeux:
State Recognition in Discrete Dynamical Systems Using Petri Nets and Evidence Theory. ESCQARU 1999: 352-361 - [c5]Nicolas Valentin, Thierry Denoeux, F. Fotoohi:
An hybrid neural network based system for optimization of coagulant dosing in a water treatment plant. IJCNN 1999: 3380-3385 - 1998
- [j6]Lalla Merieme Zouhal, Thierry Denoeux:
An evidence-theoretic k-NN rule with parameter optimization. IEEE Trans. Syst. Man Cybern. Part C 28(2): 263-271 (1998) - 1997
- [j5]Thierry Denoeux:
Analysis of evidence-theoretic decision rules for pattern classification. Pattern Recognit. 30(7): 1095-1107 (1997) - [c4]Thierry Denoeux:
Function approximation in the framework of evidence theory: a connectionist approach. ICNN 1997: 199-203 - 1996
- [j4]Régis Lengellé, Thierry Denoeux:
Training MLPs layer by layer using an objective function for internal representations. Neural Networks 9(1): 83-97 (1996) - 1995
- [j3]Thierry Denoeux, P. Rizand:
Analysis of Rainfall Forecasting using Neural Networks. Neural Comput. Appl. 3(1): 50-61 (1995) - [j2]Thierry Denoeux:
A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Trans. Syst. Man Cybern. 25(5): 804-813 (1995) - [c3]Lalla Merieme Zouhal, Thierry Denoeux:
An Adaptive k-NN Rule Based on Dempster-Shafer Theory. CAIP 1995: 310-317 - [c2]Mohamed Karouia, Régis Lengellé, Thierry Denoeux:
Performance analysis of a MLP weight initialization algorithm. ESANN 1995 - [c1]T. Trautmann, Thierry Denoeux:
Comparison of dynamic feature map models for environmental monitoring. ICNN 1995: 73-78 - 1993
- [j1]Thierry Denoeux, Régis Lengellé:
Initializing back propagation networks with prototypes. Neural Networks 6(3): 351-363 (1993)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-14 00:52 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
