Benjamin Quost

发表

Estimating energy consumption of a PHEV using vehicle and on-board navigation data

Thierry Denoeux, Abdeldjalil Ouahabi, Benjamin Quost, 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

CEVCLUS: evidential clustering with instance-level constraints for relational data

Thierry Denoeux, Violaine Antoine, Benjamin Quost, 2014, Soft Comput..

CEVCLUS: Constrained evidential clustering of proximity data

Thierry Denoeux, Violaine Antoine, Benjamin Quost, 2011, EUSFLAT Conf..

Editorial

Sébastien Destercke, Benjamin Quost, Marie-Hélène Masson, 2014, Int. J. Approx. Reason..

Clustering and classification of fuzzy data using the fuzzy EM algorithm

Thierry Denoeux, Benjamin Quost, T. Denoeux, 2016, Fuzzy Sets Syst..

Scalable Uncertainty Management: 13th International Conference, SUM 2019, Compiègne, France, December 16–18, 2019, Proceedings

Nahla Ben Amor, Benjamin Quost, Martin Theobald, 2019, SUM.

Pairwise Classifier Combination in the Framework of Belief Functions

Thierry Denoeux, Benjamin Quost, Marie-Hélène Masson, 2005 .

Inferring from an Imprecise Plackett-Luce Model: Application to Label Ranking

Sébastien Destercke, Benjamin Quost, Arthur Van Camp, 2020, SUM.

Classifier fusion in the Dempster-Shafer framework using optimized t-norm based combination rules

Thierry Denoeux, Benjamin Quost, Marie-Hélène Masson, 2011, Int. J. Approx. Reason..

Classification by pairwise coupling of imprecise probabilities

Sébastien Destercke, Benjamin Quost, 2018, Pattern Recognit..

CECM: Adding pairwise constraints to evidential clustering

Thierry Denoeux, Violaine Antoine, Benjamin Quost, 2010, International Conference on Fuzzy Systems.

CECM: Constrained evidential C-means algorithm

Thierry Denoeux, Violaine Antoine, Benjamin Quost, 2012, Comput. Stat. Data Anal..

Sound Source Localization from Uncertain Information Using the Evidential EM Algorithm

Benjamin Quost, Jérôme Antoni, Xun Wang, 2013, SUM.

Refined modeling of sensor reliability in the belief function framework using contextual discounting

Thierry Denoeux, Benjamin Quost, David Mercier, 2008, Inf. Fusion.

Soft label based semi-supervised boosting for classification and object recognition

Vincent Frémont, Benjamin Quost, Dingfu Zhou, 2014, 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV).

Logistic Regression of Soft Labeled Instances via the Evidential EM Algorithm

Benjamin Quost, 2014, Belief Functions.

Correcting Binary Imprecise Classifiers: Local vs Global Approach

Sébastien Destercke, Benjamin Quost, 2012, SUM.

Adapting a Combination Rule to Non-Independent Information Sources

Thierry Denoeux, Benjamin Quost, Marie-Hélène Masson, 2008, IPMU 2008.

On modeling ego-motion uncertainty for moving object detection from a mobile platform

Vincent Frémont, Benjamin Quost, Bihao Wang, 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

Cautious relational clustering: A thresholding approach

Sébastien Destercke, Benjamin Quost, Marie-Hélène Masson, 2020, Expert Syst. Appl..

Iterative beamforming for identification of multiple broadband sound sources

Jérôme Antoni, Benjamin Quost, Xun Wang, 2016 .

Moving object detection and segmentation in urban environments from a moving platform

Hongdong Li, Vincent Frémont, Benjamin Quost, 2017, Image Vis. Comput..

Refined classifier combination using belief functions

Thierry Denoeux, Benjamin Quost, Marie-Hélène Masson, 2008, 2008 11th International Conference on Information Fusion.

Parametric classification with soft labels using the evidential EM algorithm: linear discriminant analysis versus logistic regression

Thierry Denoeux, Benjamin Quost, Shoumei Li, 2017, Adv. Data Anal. Classif..

Method for calculating a fuel consumption and electric power management instruction of a hybrid motor vehicle

Thierry Denoeux, Benjamin Quost, Abdel-Djalil Ourabah, 2017 .

Pairwise classifier combination using belief functions

Thierry Denoeux, Benjamin Quost, Marie-Hélène Masson, 2007, Pattern Recognit. Lett..

Learning from data with uncertain labels by boosting credal classifiers

Thierry Denoeux, Benjamin Quost, T. Denoeux, 2009, U '09.

Contextual Discounting of Belief Functions

Thierry Denoeux, Benjamin Quost, David Mercier, 2005, ECSQARU.

Clustering Fuzzy Data Using the Fuzzy EM Algorithm

Thierry Denoeux, Benjamin Quost, 2010, SUM.

Combining Binary Classifiers with Imprecise Probabilities

Sébastien Destercke, Benjamin Quost, 2011, IUKM.

Dealing with Atypical Instances in Evidential Decision-Making

Sébastien Destercke, Benjamin Quost, Marie-Hélène Masson, 2020, SUM.

Decision-making from Partial Instances by Active Feature Querying

Benjamin Quost, 2021, ISIPTA.

Cautious Random Forests: a New Decision Strategy and some Experiments

Benjamin Quost, Marie-Hélène Masson, Haifei Zhang, 2021, ISIPTA.

One-against-all Classifier Combination in the Framework of Belief Functions

Benjamin Quost, 2006 .

Moving objects detection and credal boosting based recognition in urban environments

Benjamin Quost, Dingfu Zhou, Vincent Fremont, 2013, 2013 IEEE Conference on Cybernetics and Intelligent Systems (CIS).

Estimation of multiple sound sources with data and model uncertainties using the EM and evidential EM algorithms

Jérôme Antoni, Benjamin Quost, Jean-Daniel Chazot, 2016 .