Benjamin Quost
Benjamin Quost
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Refined modeling of sensor reliability in the belief function framework using contextual discounting
D Mercier, B Quost, T Denœux
Information fusion 9 (2), 246-258, 2008
Classifier fusion in the Dempster–Shafer framework using optimized t-norm based combination rules
B Quost, MH Masson, T Denœux
International Journal of Approximate Reasoning 52 (3), 353-374, 2011
CECM: Constrained evidential c-means algorithm
V Antoine, B Quost, MH Masson, T Denoeux
Computational Statistics & Data Analysis 56 (4), 894-914, 2012
Pairwise classifier combination using belief functions
B Quost, T Denœux, MH Masson
Pattern Recognition Letters 28 (5), 644-653, 2007
Clustering and classification of fuzzy data using the fuzzy EM algorithm
B Quost, T Denoeux
Fuzzy Sets and Systems 286, 134-156, 2016
Moving object detection and segmentation in urban environments from a moving platform
D Zhou, V Frémont, B Quost, Y Dai, H Li
Image and Vision Computing 68, 76-87, 2017
Parametric classification with soft labels using the evidential EM algorithm: linear discriminant analysis versus logistic regression
B Quost, T Denoeux, S Li
Advances in Data Analysis and Classification 11, 659-690, 2017
Estimation of multiple sound sources with data and model uncertainties using the EM and evidential EM algorithms
X Wang, B Quost, JD Chazot, J Antoni
Mechanical Systems and Signal Processing 66, 159-177, 2016
CEVCLUS: evidential clustering with instance-level constraints for relational data
V Antoine, B Quost, MH Masson, T Denoeux
Soft Computing 18, 1321-1335, 2014
Contextual discounting of belief functions
D Mercier, B Quost, T Denœux
European Conference on Symbolic and Quantitative Approaches to Reasoning and …, 2005
Iterative beamforming for identification of multiple broadband sound sources
X Wang, B Quost, JD Chazot, J Antoni
Journal of Sound and Vibration 365, 260-275, 2016
Learning from data with uncertain labels by boosting credal classifiers
B Quost, T Denœux
Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery From …, 2009
Classification by pairwise coupling of imprecise probabilities
B Quost, S Destercke
Pattern Recognition 77, 412-425, 2018
On modeling ego-motion uncertainty for moving object detection from a mobile platform
D Zhou, V Frémont, B Quost, B Wang
2014 IEEE Intelligent Vehicles Symposium Proceedings, 1332-1338, 2014
One-against-all classifier combination in the framework of belief functions
B Quost, T Denoeux, M Masson, A UPJV
Eighth Conference on Information Fusion Conference, 356-363, 2006
Clustering fuzzy data using the fuzzy EM algorithm
B Quost, T Denœux
International Conference on Scalable Uncertainty Management, 333-346, 2010
Adapting a combination rule to non-independent information sources
B Quost, T Denoeux, MH Masson
12th Information Processing and Management of Uncertainty in Knowledge-Based …, 2008
Pairwise classifier combination in the transferable belief model
B Quost, T Denaeux, M Masson
2005 7th international conference on information fusion 1, 8 pp., 2005
Combining binary classifiers with imprecise probabilities
S Destercke, B Quost
Integrated Uncertainty in Knowledge Modelling and Decision Making …, 2011
Method for calculating a setpoint for managing the fuel and electricity consumption of a hybrid motor vehicle
A Ourabah, X Jaffrezic, A Gayed, B Quost, T Denoeux
US Patent 10,668,824, 2020
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