Thierry Denoeux
- Heuristique et Diagnostic des Systèmes Complexes [Compiègne] (Heudiasyc)
- Institut universitaire de France (IUF)
Présentation
Thierry Denoeux graduated from École nationale des ponts et chaussées and earned a PhD from the same institution. He is currently a Full Professor (Exceptional Class) with the Department of Information Processing Engineering at Université de Technologie de Compiègne, France. He is the president of the Belief Functions and Applications Society. In 2019, he was appointed as a senior member of Institut Universitaire de France, and he was reconducted in 2024. His research interests concern reasoning and decision-making under uncertainty and, more generally, the management of uncertainty in intelligent systems. His main contributions are in the theory of belief functions with applications to statistical inference, machine learning and information fusion. He is the author of more than 350 papers in journals and conference proceedings and he has supervised more than 30 PhD theses. He is the Editor-in-Chief of the International Journal of Approximate Reasoning (Elsevier), and an Associate Editor of several journals including Fuzzy Sets and Systems and International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems.
Publications
Publications
|
|
Uncertainty quantification in regression neural networks using likelihood-based belief functions8th International Conference on Belief Functions (BELIEF 2024), Sep 2024, Belfast, United Kingdom. pp.40-48, ⟨10.1007/978-3-031-67977-3_5⟩ |
|
|
An evidential time-to-event prediction model based on Gaussian random fuzzy numbers8th International Conference on Belief Functions (BELIEF 2024), Sep 2024, Belfast, United Kingdom. pp.49-57, ⟨10.1007/978-3-031-67977-3_6⟩ |
|
|
Combination of Dependent Gaussian Random Fuzzy Numbers8th International Conference on Belief Functions (BELIEF 2024), Sep 2024, Belfast, United Kingdom. pp.264-272, ⟨10.1007/978-3-031-67977-3_28⟩ |
|
|
r-ERBFN : an Extension of the Evidential RBFN Accounting for the Dependence Between Positive and Negative Evidence16th International Conference on Scalable Uncertainty Management (SUM 2024), Nov 2024, Palermo, Italy. pp.354-368, ⟨10.1007/978-3-031-76235-2_26⟩ |
|
|
Algebraic Product Is the Only "And-like" Operation for Which Normalized Intersection Is Associative: A Proof5th International Conference on Artificial Intelligence and Computational Intelligence (AICI 2024), Jan 2024, Hanoi (Vietnam), Vietnam. pp.47-53, ⟨10.1007/978-3-031-63929-6_6⟩ |
|
|
Belief Functions on the Real Line defined by Transformed Gaussian Random Fuzzy NumbersIEEE International Conference on Fuzzy Systems (FUZZ 2023), IEEE, Aug 2023, Songdo Incheon, South Korea. pp.1-6, ⟨10.1109/FUZZ52849.2023.10309755⟩ |
|
|
Stable clustering ensemble based on evidence theoryIEEE International Conference on Image Processing (ICIP 2022), Oct 2022, Bordeaux, France. pp.2046-2050, ⟨10.1109/ICIP46576.2022.9897984⟩ |
|
|
Evidence fusion with contextual discounting for multi-modality medical image segmentation25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), Sep 2022, Singapour, Singapore. pp.401-411, ⟨10.1007/978-3-031-16443-9_39⟩ |
|
|
Trusted Multi-View Deep Learning with Opinion Aggregation36th AAAI Conference on Artificial Intelligence (AAAI-22), Feb 2022, Virtual conference, United States. pp.7585-7593, ⟨10.1609/aaai.v36i7.20724⟩ |
|
|
A Distributional Approach for Soft Clustering Comparison and Evaluation7th International Conference on Belief Functions (BELIEF 2022), Oct 2022, Paris, France. pp.3-12, ⟨10.1007/978-3-031-17801-6_1⟩ |
|
|
An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers7th International Conference on Belief Functions (BELIEF 2022), Oct 2022, Paris, France. pp.57-66, ⟨10.1007/978-3-031-17801-6_6⟩ |
|
|
Covid-19 classification with deep neural network and belief functionsThe Fifth International Conference on Biological Information and Biomedical Engineering (BIBE2021), Jul 2021, Hangzhou, China. pp.1-4, ⟨10.1145/3469678.3469719⟩ |
|
|
Deep Neural Networks with Prior Evidence for Bladder Cancer StagingIEEE International Conference on Bioinformatics and Biomedicine (BIBM 2021), Dec 2021, Houston, United States. pp.1221-1226, ⟨10.1109/BIBM52615.2021.9669848⟩ |
|
|
Belief function-based semi-supervised learning for brain tumor segmentation18th IEEE International Symposium on Biomedical Imaging (ISBI 2021), Apr 2021, Nice, France. pp.160-164, ⟨10.1109/ISBI48211.2021.9433885⟩ |
|
|
Fusion of evidential CNN classifiers for image classification6th International Conference on Belief Functions (BELIEF 2021), Oct 2021, Shanghai, China. pp.168-176, ⟨10.1007/978-3-030-88601-1_17⟩ |
|
|
Deep PET/CT Fusion with Dempster-Shafer Theory for Lymphoma SegmentationInternational Workshop on Machine Learning in Medical Imaging (MLMI 2021), Sep 2021, Strasbourg, France. pp.30-39, ⟨10.1007/978-3-030-87589-3_4⟩ |
|
|
Evidential Segmentation of 3D PET/CT Images6th International Conference on Belief Functions (BELIEF 2021), Sep 2021, Shanghai, China. pp.159-167, ⟨10.1007/978-3-030-88601-1_16⟩ |
|
|
Segmentation de séries temporelles par modèles de mélange avec contraintes sur les instants d’apparition des classes28èmes Rencontres Francophones sur la Logique Floue et ses Applications (LFA 2019), Nov 2019, Alès, France |
|
|
ConvNet and Dempster-Shafer Theory for Object Recognition13th international conference on Scalable Uncertainty Management (SUM 2019), Dec 2019, Compiègne, France. pp.368-381, ⟨10.1007/978-3-030-35514-2_27⟩ |
|
|
Multistep Prediction using Point-Cloud Approximation of Continuous Belief FunctionsIEEE International Conference on Fuzzy Systems (FUZZ 2019), Jun 2019, New Orleans, United States. pp.1-6, ⟨10.1109/FUZZ-IEEE.2019.8858988⟩ |
|
|
Making Set-valued Predictions in Evidential Classification: A Comparison of Different Approaches11th International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA 2019), Jun 2019, Gand, Belgium. pp.276-285 |
|
|
MACHINE LEARNING AS A DECISION SUPPORT TOOL FOR WASTEWATER TREATMENT PLANT OPERATIONWATER RESOURCES MANAGEMENT 2019, May 2019, Alicante, Spain. pp.103-107, ⟨10.2495/WRM190101⟩ |
|
|
An Axiomatic Utility Theory for Dempster-Shafer Belief Functions11th International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA 2019), Jun 2019, Gand, Belgium. pp.145-155 |
|
|
Collaborative Evidential ClusteringFuzzy Techniques: Theory and Applications - International Fuzzy Systems Association World Congress (IFSA/NAFIPS 2019), Jun 2019, Louisiana, United States. pp.518-530, ⟨10.1007/978-3-030-21920-8_46⟩ |
|
|
Flexibility of drinking water systems: An opportunity to reduce CO2 emissions.8th International conference on Energy and Sustainability, 2019, Coimbra, Portugal. pp.134-144 |
|
|
An Evidential K-Nearest Neighbor Classifier Based on Contextual Discounting and Likelihood Maximization5th International Conference on Belief Functions (BELIEF 2018), Sep 2018, Compiègne, France. pp.155-162, ⟨10.1007/978-3-319-99383-6_20⟩ |
Optimisation de la flexibilité énergétique des systèmes d’eau potable sur les marchés de l’énergie19e congrès annuel de la Société Française de Recherche Opérationnelle et d'Aide à la Décision (ROADEF 2018), 2018, Lorient, France |
|
|
|
Logistic regression revisited: belief function analysis5th International Conference on Belief Functions (BELIEF 2018), Sep 2018, Compiègne, France. pp.57-64, ⟨10.1007/978-3-319-99383-6_8⟩ |
|
|
A linear programming approach to optimize demand response for water systems under water demand uncertainties7th IEEE International Conference on Smart Grid and Clean Energy Technologies (ICSGCE 2018), 2018, Kajang, Malaysia. pp.206-211, ⟨10.1109/ICSGCE.2018.8556696⟩ |
|
|
Tumor delineation in FDG-PET images using a new evidential clustering algorithm with spatial regularization and adaptive distance metric14th IEEE International Symposium on Biomedical Imaging (ISBI 2017), Apr 2017, Melbourne, Australia. pp.1177-1180, ⟨10.1109/ISBI.2017.7950726⟩ |
|
|
Distributed data fusion in the Dempster-Shafer framework2017 12th System of Systems Engineering Conference (SoSE), Jun 2017, Waikoloa, United States. pp.1-6, ⟨10.1109/SYSOSE.2017.7994954⟩ |
|
|
Constrained interval-valued linear regression model20th International Conference on Information Fusion (FUSION 2017), Jul 2017, Xi'an, China. pp.1-8, ⟨10.23919/ICIF.2017.8009676⟩ |
|
|
Accurate Tumor Segmentation In FDG-PET Images With Guidance Of Complementary CT ImagesIEEE International Conference on Image Processing (ICIP 2017), Sep 2017, Beijing, China. pp.4447-4451, ⟨10.1109/ICIP.2017.8297123⟩ |
|
|
Scheduling Demand Response on the French Spot Power Market for Water Distribution Systems by Optimizing the Pump Scheduling.3th Workshop on Models and Algorithms for Planning and Scheduling Problems (MAPSP 2017), 2017, Seeon-Seebruck, Germany. pp.172-174 |
|
|
Identification of Elastic Properties Based on Belief Function InferenceFourth International Conference Belief Functions: Theory and Applications (BELIEF 2016), Sep 2016, Prague, Czech Republic. pp.182-189, ⟨10.1007/978-3-319-45559-4_19⟩ |
|
|
Beyond Fuzzy, Possibilistic and Rough: An Investigation of Belief Functions in Clustering8th International Conference on Soft Methods in Probability and Statistics (SMPS 2016), Sep 2016, Rome, Italy. pp.157-164, ⟨10.1007/978-3-319-42972-4_20⟩ |
Joint Feature Transformation and Selection Based on Dempster-Shafer Theory16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU-2016), Jun 2016, Eindhoven, Netherlands. pp.253-261, ⟨10.1007/978-3-319-40596-4_22⟩ |
|
|
|
Evidential Clustering: A Review5th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2016), Nov 2016, Da Nang, Vietnam. pp.24-35, ⟨10.1007/978-3-319-49046-5_3⟩ |
|
|
k-EVCLUS: Clustering Large Dissimilarity Data in the Belief Function Framework4th International Conference on Belief Functions: Theory and Applications (BELIEF 2016), Sep 2016, Prague, Czech Republic. pp.105-112, ⟨10.1007/978-3-319-45559-4_11⟩ |
|
|
Robust Cancer Treatment Outcome Prediction Dealing with Small-Sized and Imbalanced Data from FDG-PET Images19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2016), Oct 2016, Athène, Greece. pp.61-69, ⟨10.1007/978-3-319-46723-8_8⟩ |
Cancer Therapy Outcome Prediction based on Dempster-Shafer Theory and PET ImagingAAPM meeting, Jul 2015, Anaheim California, United States |
|
|
|
Evidential multinomial logistic regression for multiclass classifier calibration18th International Conference on Information Fusion, Jul 2015, Washington D.C., United States. pp.1106-1112 |
|
|
Identification en contexte incertain par la théorie des fonctions de croyance12e Colloque national en calcul des structures, CSMA, May 2015, Giens, France |
|
|
Dempster-Shafer theory based feature selection with sparse constraint for outcome prediction in cancer therapyIn MICCAI - International Workshop on Machine Learning in Medical Imaging, 2015, Munich, Germany. ⟨10.1007/978-3-319-24574-4_83⟩ |
Estimating the energy consumption of a PHEV using vehicle and on-board navigation data2015 IEEE Intelligent Vehicles Symposium (IV 2015), Jun 2015, Seoul, South Korea. pp.755-760 |
|
|
|
Fusion of Pairwise Nearest-Neighbor Classifiers Based on Pairwise-Weighted Distance Metric and Dempster-Shafer Theory17th International Conference on Information Fusion, Jul 2014, Salamanca, Spain. pp.1-17 |
|
|
Evidential Distributed Dynamic Map for Cooperative Perception in VANetsIEEE intelligent Vehicles Symposium (IV 2014), Jun 2014, Dearborn, Michigan, United States. pp.1421-1426 |
|
|
Predicting Stock Returns in the Capital Asset Pricing Model Using Quantile Regression and Belief FunctionsThird International Conference Belief Functions: Theory and Applications, F. Cuzzolin, Sep 2014, Oxford, United Kingdom. p. 219-226, ⟨10.1007/978-3-319-11191-9_24⟩ |
Evidential Logistic Regression for Binary SVM Classifier CalibrationThird International Conference on Belief Functions (BELIEF 2014), Sep 2014, Oxford, United Kingdom. pp.49-57 |
|
|
|
Transformation de scores SVM en fonctions de croyance19ème congrès national sur la Reconnaissance de Formes et l'Intelligence Artificielle (RFIA'14), Jun 2014, Rouen, France |
|
|
Evidential combination of pedestrian detectorsBritish Machine Vision Conference, Sep 2014, Nottingham, United Kingdom. pp.1-14 |
|
|
Estimation and Prediction Using Belief Functions: Application to Stochastic Frontier Analysis8th International Conference of the Thailand Econometric Society, Jan 2015, Chiang-Mai, Thailand. pp.171-184, ⟨10.1007/978-3-319-13449-9_12⟩ |
|
|
Training and evaluating classifiers from evidential data: application to E2M tree pruningThird International Conference on Belief Functions, BELIEF 2014, Sep 2014, Oxford, United Kingdom |
|
|
Application of E2M Decision Trees to Rubber Quality PredictionInternational Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2014), Jul 2014, Montpellier, France. pp.107-116, ⟨10.1007/978-3-319-08795-5_12⟩ |
|
|
Fusion d'informations sur des images sursegmentées : Une application à la compréhension de scènes routièresOrasis, Congrès des jeunes chercheurs en vision par ordinateur, Jun 2013, Cluny, France |
|
|
Information Fusion on Oversegmented Images: An Application for Urban Scene UnderstandingThirteenth IAPR International Conference on Machine Vision Applications, May 2013, Kyoto, Japan. pp.189-193 |
|
|
Optimal object association from pairwise evidential mass functions16th International Conference on Information Fusion (FUSION 2013), Jul 2013, Istanbul, Turkey. pp.774-780 |
|
|
Learning decision trees from uncertain data with an evidential EM approach12th International Conference on Machine Learning and Applications (ICMLA 2013), Dec 2013, Miami, United States. pp.1-6 |
|
|
Using Dempster-Shafer Theory to model uncertainty in climate change and environmental impact assessmentsInternational Conference on Information Fusion, Jul 2013, Istanbul, Turkey. pp.2117-2124 |
|
|
Information fusion and evidential grammars for object class segmentationFifth IROS Workshop on Planning, Perception and Navigation for Intelligent Vehicles, Nov 2013, Tokyo, Japan. pp.165-170 |
|
|
Evidential Grammars for Image Interpretation. Application to multimodal traffic scene understandingThird International Symposium on Integrated Uncertainty in Knowledge Modeling and Decision Making, Jul 2013, Beijing, China. pp.65-78 |
|
|
Distributed Data fusion for detecting Sybil attacks in VANETs2nd International Conference on Belief Functions (BELIEF 2012), May 2012, Compiègne, France. pp.351-358, ⟨10.1007/978-3-642-29461-7_41⟩ |
|
|
Partially-Hidden Markov Models.2nd International Conference on Belief Functions, BELIEF'12., May 2012, Compiègne, France. pp.1-8 |
|
|
Fusion distribuée évidentielle pour la détection d'attaques sybil dans un réseau de véhicules.LFA 2012, Nov 2012, Compiègne, France. pp.63-70 |
|
|
Ranking from pairwise comparisons in the belief functions framework2nd International Conference on Belief Functions (BELIEF 2012), May 2012, Compiègne, France. pp.311-318 |
|
|
Arbres de classification construits à partir de fonctions de croyanceRencontres francophones sur la Logique Floue et ses Applications (LFA 2012), Nov 2012, Compiègne, France |
|
|
Classification trees based on belief functions2nd International Conference on Belief Functions (BELIEF 2012), May 2012, Compiègne, France. pp.77-84, ⟨10.1007/978-3-642-29461-7_9⟩ |
Conditioning in Dempster-Shafer Theory: Prediction vs. Revision2nd International Conference on Belief Functions (2012), May 2012, Compiègne, France. pp.385-392, ⟨10.1007/978-3-642-29461-7_45⟩ |
|
|
|
Self-stabilizing Distributed Data Fusion14th International Symposium on Stabilization, Safety, and Security of Distributed Systems (SSS 2012), Oct 2012, Toronto, Canada. pp.148-162, ⟨10.1007/978-3-642-33536-5_15⟩ |
|
|
CEVCLUS: Constrained evidential clustering of proximity data7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2011), Aug 2011, Aix-Les-Bains, France. pp.876-882 |
Ordonnancement d'alternatives dans le cadre de la théorie des fonctions de croyance.Rencontres Francophones sur la Logique Floue et ses Applications, Nov 2010, Lannion, France. pp.161-167 |
|
|
|
Semi-supervised feature extraction using independent factor analysisICOR, 9th International Conference on Operations Research, Feb 2010, La Havanne, Cuba. 8p |
CECM: Adding pairwise constraints to evidential clustering2010 IEEE International Conference on Fuzzy Systems (Fuzz'IEEE 2010), Jul 2010, Barcelona, Spain. pp.879-886 |
|
Evidential multi-Label classification approach to learning from data with imprecise labels13th Int. Conf. on Information Processing and Management of Uncertainty (IPMU 2010), Jun 2010, Dortmund, Germany. pp.119-128 |
|
Fuzzy Multi-Label Learning Under Veristic Variables2010 IEEE International Conference on Fuzzy Systems (FUZZ‐IEEE 2010), Jul 2010, Barcelona, Spain. pp.1696-1703 |
|
Statistical Inference with Belief Functions and Possibility Measures : a discussion of basic assumptions5th International Conference on Soft Methods in Probability and Statistics (SMPS 2010), Sep 2010, Oviedo, Spain. pp.217-225, ⟨10.1007/978-3-642-14746-3_27⟩ |
|
Clustering fuzzy data using the fuzzy EM algorithmFourth International Conference on Scalable Uncertainty Management (SUM 2010), Sep 2010, Toulouse, France. pp.333-346 |
|
Maximal likelihood from evidential data: an extension of the EM algorithmSoft Methods in Probability and Statistics (SMPS 2010), Sep 2010, Oviedo, Spain. pp.181-188 |
|
|
|
Belief Functions and Cluster EnsemblesECSQARU 2009, Jul 2009, Verona, Italy. pp.323-334 |
|
|
Multisensor data fusion for OD matrix estimationIEEE International Conference on Systems, Man and Cybernetics, Oct 2009, San Antonio, United States. pp.1-6 |
|
|
Fuzzy modelling of sensor data for the estimation of an origin-destination matrixIFSA/EUSFLAT 2009, Jul 2009, Lisbon, Portugal. pp.849-854 |
|
|
Interpretation and Computation of alpha-Junctions for Combining Belief Functions6th International Symposium on Imprecise Probability: Theories and Applications (ISIPTA '09), 2009, Durham, United Kingdom |
Pertinence et Sincérité en Fusion d'InformationsRencontres Francophones sur la Logique Floue et ses Applications (LFA 2009), 2009, Annecy, France. pp.23-30 |
|
A state estimation method for multiple model systems using belief function theoryFUSION '09, Jul 2009, Seattle, Washington, United States. pp.506-513 |
|
Learning from data with uncertain labels by boosting credal classifiersThe 15th ACM SIGKDD Conference on Knwledge Discovery and Data Mining, Jun 2009, Paris, France. pp.38-47 |
|
ECM : Algorithme évidentiel des c-moyennes avec contraintesRencontres Francophones sur la Logique Floue et ses Applications (LFA 2009),, 2009, Annecy, France. pp.275-282 |
|
Map matching algorithm using interval analysis and Dempster-Shafer theoryIEEE IVS09, Jun 2009, China. pp.494 - 499, ⟨10.1109/IVS.2009.5164328⟩ |
|
|
|
Short-time OD matrix estimation for a complex junction using Fuzzy-Timed High-Level Petri Nets12th International IEEE Conference on Intelligent Transportation Systems, Oct 2009, St. Louis, United States. pp.1-6 |
An Evidence-Theoretic k-Nearest Neighbor Rule for Multi-label ClassificationSUM 2009, Sep 2009, United States. pp.297-308, ⟨10.1007/978-3-642-04388-8_23⟩ |
|
Analyse en composantes indépendantes parcimonieuse pour le diagnostic de systèmes répartisXXe colloque GRETSI, 2009, Dijon, France |
|
Pertinence et sincérité en fusion d'informationsRencontres Francophones sur la Logique Floue et ses Applications (LFA 2009), Nov 2009, Annecy, France. pp.23-30 |
|
Statistical inference using belief functions: a reappraisal of General Bayes TheoremSecond Workshop of the ERCIM working group on Computing and Statistics (ERCIM 2009), ERCIM: European Consortium for Informatics and Mathematics, Oct 2009, Limassol, Cyprus |
|
|
|
Partially-supervised learning in Independent Factor AnalysisEuropean Symposium on Artificial Neural Networks (ESANN), Apr 2009, Bruges, Belgium. pp.53--58 |
A new method for state estimation of dynamic system based on Dempster Shafer theoryACTEA apos;09, Jul 2009, Lebanon. pp.101 - 106, ⟨10.1109/ACTEA.2009.5227922⟩ |
|
|
|
Noiseless Independent Factor Analysis with Mixing Constraints in a Semi-supervised Framework. Application to Railway Device Fault DiagnosisInternational Conference on Artificial Neural Network, Sep 2009, Limassol, Cyprus. pp.416-425 |
|
|
Refined classifier combination using belief functions11th International Conference on Information Fusion (FUSION ‘08), Jul 2008, Germany. p. 776-782 |
|
|
Adapting a Combination Rule to Non-Independent Information Sources12th Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2008), Jul 2008, Spain. p. 448-455 |
|
|
Mixture model estimation with soft labelsFourth International Workshop on Soft Methods in Probabilities and Statistics, Sep 2008, Toulouse, France. pp.165-174 |
|
|
Noiseless Independent Factor Analysis with mixing constraints in a semi-supervised framework. Application to railway device fault diagnosis.International Conference on Artificial Neural Networks (ICANN),, Sep 2009, Limassol, Cyprus. pp.416-425, ⟨10.1007/978-3-642-04277-5_42⟩ |
General Correction Mechanisms for Weakening or Reinforcing Belief Functions2006 9th International Conference on Information Fusion, Jul 2006, Florence, France. pp.1-7, ⟨10.1109/ICIF.2006.301594⟩ |
|
|
|
Vers un Modèle de Fusion de Décisions de Lecteurs d'Adresses Postales Basé sur la Théorie des Fonctions de CroyanceSep 2006, pp.79-84 |
One-against-all combination in the framework of belief functionsInformation Processing with Management of Uncertainty in Knowledge-based Systems, 2006, Paris, France. pp.356-363 |
|
Pairwise Classifier Combination in the Framework of Belief FunctionsInternational Conference on Information Fusion, Jul 2005, Philadelphie, PA, United States. pp.xxx-xxx |
|
Contextual discounting of belief functionsEuropean Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Jul 2005, Barcelone, Spain. pp.552-562 |
|
Combinaison de classifieurs binaires dans le cadre du Modèle des Croyances TransférablesRencontres Francophones sur la Logique Floue et ses Applications (LFA), Nov 2004, Nantes, France. pp.123-130 |
|
Handling different forms of uncertainty in regression analysis: a fuzzy belief structure approachSymbolic and quantitative approaches to reasoning and uncertainty (ECSQARU\\\'99), 1999, London (U.K.), Unknown Region. pp.340--351 |
|
Regression analysis using fuzzy evidence theory.Proceedings of FUZZ-IEEE\'99, 1999, Seoul, South Korea. pp.1229--1234 |
|
Application de la théorie des fonctions de croyance en régressionRencontres Francophones sur la Logique Floue et ses Applications, 1999, Valenciennes, France. pp.13--20 |
|
A Neuro-Fuzzy model for missing data reconstructionIEEE Workshop on Emerging Technologies, Intelligent Measurement and Virtual Systems, 1998, Saint-Paul (MN, USA), Unknown Region |
|
A Fuzzy-neuro system for reconstruction of multi-sensor informationFuzzy-Neuro Systems\'98, 1998, Munich, Germany. pp.322--329 |
|
Performance analysis of a MLP weight initialization procedureESANN'95, Apr 1995, Bruxelles, Belgium |
|
Influence of weight initialization on generalization performanceICANN'95, Oct 1995, Paris, France |
|
Weight initialization in BP networks using discriminant analysis techniquesNeuro-Nimes 1994, Dec 1994, Nimes, France |
|
Performance comparison of two constructive algorithms for multilayer perceptronsANNIE'93, 1993, Saint-Louis, United States |
|
Interpolation of stationary non linear time series by an optimized neural networkICANN'93, SEPTEMBRE 1993, Sep 1993, Amsterdam, Netherlands |
|
Automatic construction of multilayer networks for non linear regressionICANN'93, Sep 1993, Amsterdam, Netherlands |
|
Production rules generation and refinement in back-propagation networksANNIE'92, Nov 1992, Saint-Louis, United States |
|
Optimizing multilayer networks layer per layer without backpropagationICANN'92, Sep 1992, Brighton, United Kingdom |
|
Ambiguity and distance rejection using multilayer networksANNIE'91, Oct 1991, Saint Louis, United States |
|
Initialization of weights in a feedforward network using prototypesICANN-91, Jun 1991, Espoo, Finland |
|
|
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation2024 |
|
|
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)2019 |
|
|
Belief Functions: Theory and Applications6th International Conference (BELIEF 2021), Oct 2021, Shanghai, China. 12915, Springer International Publishing, 2021, Lecture Notes in Computer Science, ⟨10.1007/978-3-030-88601-1⟩ |
|
|
Integrated Uncertainty in Knowledge Modelling and Decision Making.6th International Symposium (IUKM 2018), springer, 2018, ⟨10.1007/978-3-319-75429-1⟩ |
Belief Functions: Theory and Applications5th International Conference on Belief Functions (BELIEF 2018), Sep 2018, Compiègne, France. 11069, Springer, 2018, Lecture Notes in Computer Science |
|
|
|
Symbolic and Quantitative Approaches to Reasoning with UncertaintyEuropean Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty 2015, Jul 2015, Compiègne, France. 9161, Springer, 2015, Lecture Notes in Computer Science, ⟨10.1007/978-3-319-20807-7⟩ |
|
|
Representations of Uncertainty in Artificial Intelligence: Probability and PossibilityA Guided Tour of Artificial Intelligence Research Volume I: Knowledge Representation, Reasoning and Learning, Springer International Publishing, pp.69-117, 2020, ⟨10.1007/978-3-030-06164-7_3⟩ |
|
|
Representations of Uncertainty in AI: Beyond Probability and PossibilityA Guided Tour of Artificial Intelligence Research (vol. I), Springer International Publishing, pp.119-150, 2020, ⟨10.1007/978-3-030-06164-7_4⟩ |
|
|
Evidential Deep Neural Networks for Uncertain Data ClassificationKnowledge Science, Engineering and Management (Proceedings of KSEM 2020), Springer Verlag, pp.427-437, 2020, Lecture Notes in Computer Science, ⟨10.1007/978-3-030-55393-7_38⟩ |
|
|
Quality of Information Sources in Information FusionInformation Quality in Information Fusion and Decision Making, Springer, pp.31-49, 2019, ⟨10.1007/978-3-030-03643-0_2⟩ |
|
|
Quantifying Predictive Uncertainty Using Belief Functions: Different Approaches and Practical ConstructionKreinovich, V.; Sriboonchitta, S.; Chakpitak, N. Predictive Econometrics and Big Data, 753, Springer, pp.157-176, 2018, Studies in Computational Intelligence-International Journal of Approximate Reasoning, ⟨10.1007/978-3-319-70942-0_8⟩ |
Représentations de l'incertitude en intelligence artificielleMarquis, Pierre; Papini, Odile; Prade, Henri. Panorama de l'Intelligence Artificielle, 1 : Représentation des connaissances et formalisation des raisonnements (Chapitre 3), Cépaduès Editions, pp.65--121, 2014, 978-2364930414 |
|
|
|
Partially-Hidden Markov Models.T. Denoeux & M.H. Masson. Belief Functions : Theory and Applications, AISC 164. Proceedings of the 2nd International Confferencr on Belief Functions, Compiègne, 9-11 Mai 2012., 164, T. Denoeux & M.H. Masson, pp.359-366, 2012, Springer-Verlag Berlin Heidelberg 2012, 978-3-642-29460-0. ⟨10.1007/978-3-642-29461-742⟩ |
Belief Function Correction MechanismsFoundations of Reasoning under Uncertainty, 249, Springer Berlin Heidelberg, pp.203-222, 2010, Studies in Fuzziness and Soft Computing, ⟨10.1007/978-3-642-10728-3_11⟩ |
|
|
|
Dempster-Shafer reasoning in large partially ordered sets: Applications in Machine LearningV.-N. Huyn, Y. Nakamori, J. Lawry and M. Inuigushi. Integrated Uncertainty Management and Applications, 68, Springer Berlin Heidelberg, pp.39-54, 2010, Advances in Intelligent and Soft Computing, ⟨10.1007/978-3-642-11960-6_5⟩ |
|
|
Confidence Management in Vehicular NetworkH. Moustafa and Y. Zhang. Vehicular Networks: Techniques, Standards and Applica, Taylor and Francis, pp.355-377, 2009, CRC Press |
|
|
Uncertainty Modelling in Data ScienceSpringer, 2019, ⟨10.1007/978-3-319-97547-4⟩ |
Proceedings of Eighth International Symposium on Imprecise Probability: Theories and ApplicationUniversité de Technologie de Compiègne, pp.412, 2013, 978-2-913923-35-5 |
Procédé de calcul d’une consigne de gestion de la consommation en carburant et en courant électrique d’un véhicule automobile hybrideFrance, N° de brevet: FR3061470. 2018 |
|
Procédé d’optimisation de la consommation énergétique d’un véhicule hybrideFrance, N° de brevet: FR3061471. 2018 |
|
Procédé de calcul d’une consigne de gestion de la consommation en carburant et en courant électrique d’un véhicule automobile hybrideFrance, N° de brevet: FR3038277. 2017 |
Robust cancer treatment outcome prediction dealing with small-sized and imbalanced data from FDG-PET imagesMICCAI, Oct 2016, Athènes, Greece |
|
Dempster-Shafer theory based outcome prediction in cancer therapyColloque du Groupe d’Etude du Traitement du Signal et des Images, 2015, Lyon, France |
|
Outcome prediction in tumour therapy based on dempster-shafer TheoryInternational Symposium on Biomedical Imaging, 2015, New-York, United States. ⟨10.1109/ISBI.2015.7163817⟩ |
|
|
Fiabilité de la prévision de pluie par radar en hydrologie urbaineHydrologie. Ecole Nationale des Ponts et Chaussées, 1989. Français. ⟨NNT : ⟩ |