Pre-prints

C. Bouveyron, Etienne Côme, J. Jacques. The Discriminative Functional Mixture Model for the Analysis of Bike Sharing Systems [hal]


International journals

E. Côme, P. Latouche. Model selection and clustering in stochastic block models with the exact integrated complete data likelihood. To appear in Statistical Modelling.[arXiv], [pdf]

P.A. Laharotte, R. Billot, E. Côme, L. Oukhellou, A. Nantes, N.E. El Faouzi. Spatiotemporal Analysis of Bluetooth Data: Application to a Large Urban Network. Transactions on Intelligent Transportation Systems (99) : 1-10, 2014.

E. Côme, L. Oukhellou. Model-based count series clustering for Bike-sharing system usage mining, a case study with the Vélib’ system of Paris. ACM TIST 5(3), 21014. [pdf]

A. Randriamanamihaga, E. Côme, L. Oukhellou and G. Govaert. Clustering the Vélib’ origin-destinations flows by means of Poisson mixture models. Neurocomputing 141(2) : 124–138, 2014. [preprint]

E. Côme and E. Diemert. The Noise Cluster Model, a Greedy Solution to the Network Community Extraction Problem. I3, 11(3), 2011. [pdf]

Z. Cherfi, E. Côme, L. Oukhellou, T. Denoeux and P. Aknin. Partially supervised Independent Factor Analysis using soft labels elicited from multiple experts: application to railway track circuit diagnosis. Soft Computing, 16(5) :741-754, 2012.

E. Côme, L. Oukhellou, T. Denoeux and P. Aknin. Fault diagnosis of a railway device using semi-supervised independent factor analysis with mixing constraints. Pattern Analysis and Applications, 15(3) :313-326, 2012. [pdf]

E. Côme, L. Oukhellou, T. Denoeux and P. Aknin. Learning from partially supervised data using mixture models and belief functions. Pattern Recognition,42(3) :334–348, 2009. [pdf]

L. Oukhellou, E. Côme, L. Bouillaut and P. Aknin. Combined use of sensor data and structural data processed by bayesian network. application to a railway diagnosis aid scheme. Transportation Research Part C,16(6) :755–767, 2008.

A. Samé, L. Oukhellou, E. Côme, and P. Aknin. Mixture-model-based signal denoising. Advances in Data Analysis and Classification (ADAC), 1(1):39–51, 2007. [pdf]


International Conferences

E. Côme, M.K. El Mahrsi, L. Oukhellou. Cartographie interactive de matrices Origines / Destinations. In proceedings of Spatial Analysis and GEOmatics, 2014, Grenoble.

M.K. El Mahrsi, E. Côme, J. Baro and L. Oukhellou . Understanding Passenger Patterns in Public Transit Through Smart Card and Socioeconomic Data. In 3rd International Workshop on Urban Computing (SigKDD), 2014. [pdf]

E. Côme, A. Randriamanamihaga, L. Oukhellou and P. Aknin. Spatio-temporal analysis of Dynamic Origin-Destination data using Latent Dirichlet Allocation. Application to the Vélib’ Bike Sharing System of Paris. In Proceedings of 93rd Annual Meeting of the Transportation Research Board, 2014. [pdf]

Y. Han, E. Côme and L. Oukhellou. Towards bicycle demand prediction of large-scale bicycle sharing system. In Proceedings of 93rd Annual Meeting of the Transportation Research Board, 2014. [pdf]

A. Randriamanamihaga, E. Côme, L. Oukhellou and G. Govaert. Clustering the Vélib’ origin-destinations flows by means of Poisson mixture models. In Proceedings of the European Symposium on Artificial Neural Networks, 2013. [pdf]

J. Baro, E. Côme, P. Aknin and O. Bonin. Hierarchical and multiscale Mean Shift segmentation of population grid. In Proceedings of the European Symposium on Artificial Neural Networks, 2013. [pdf]

E. Côme, M. Cottrell, M. Verleyssen and J. Lacaille. Self Organizing Star (SOS) for health monitoring. In Proceedings of the European Symposium on Artificial Neural Networks, 2010.

E. Côme, L. Oukhellou, T. Denoeux and P. Aknin Noiseless Independent Factor Analysis with mixing constraints in a semi-supervised framework. Application to railway device fault diagnosis. In Proceedings of the 7th International Conference on Artificial Neural Networks (ICANN), Accepté, 2009. [pdf]

Z.L Cherfi, L. Oukhellou, E. Côme, and P. Aknin. Railway device diagnosis using sparse independent component analysis. In Proceedings of the European Signal Processing Conference, Accepté, 2009.

E. Côme, L. Oukhellou, P. Aknin. and T. Denoeux. Partially-supervised learning in Independent Factor Analysis. In Proceedings of the European Symposium on Artificial Neural Networks, Accepté, 2009.

E. Côme, Z.L Cherfi, L. Oukhellou, T. Denoeux, and P. Aknin. Semi-supervised IFA with prior knowledge on the mixing process An application to a railway device diagnosis. In International Conference on Machine Learning and Applications,December 11-13, San-Diego, 2008.

E. Côme, L. Oukhellou, T. Denoeux, and P. Aknin. Mixture model estimation with soft labels. In Proceedings of the 4st Soft Methods in Probability and Statistics, June 13-15, Toulouse, 2008. [pdf]

E. Côme, L. Bouillaut, P. Aknin, and L. Oukhellou. Hidden markov random field, an application to railway infrastructure diagnosis. In Proceedings of the 1st IFAC Workshop on dependable control of discret systems (DCDS), June 13-15, Paris, pages 155–160, 2007. [pdf]

E. Côme, L. Bouillaut, P. Aknin, and A. Samé. Bayesian network for railway infrastructure diagnosis. In Proceedings of the 11th IPMU International Conference on Information Processing and Management of Uncertainty (IPMU), 2006.

A. Debiolles, L. Oukhellou, P. Aknin, T. Denoeux, and E. Côme. Linear and non linear regression using PLS feature selection and NN on a defect diagnosis application. In Proceedings of the International Conference On Machine Intelligence (ICMI), Tozeur, Tunisia}, 2005.


French conferences

E. Côme, M.K. El Marhsi and L. Oukhellou. Cartographie interactive de matrices Origines / Destinations. In Sagéo 2014. [pdf]

E. Côme, E. Diemert. The noise cluster model . In Marami 2010. [pdf]

E. Côme, L. Oukhellou , P. Aknin, and T. Denoeux. Diagnostic de systèmes spatialement répartis, modèle génératif et méthode à noyau. In XXI Colloque GRETSI, 11-14 Septembre 2007, Troyes, pages 633–636, 2007.

E. Côme. Diagnostic de systèmes spatialement répartis, par modèle génératif et méthode à noyau. Application au diagnostic des circuits de voie ferroviaires. In J. Marais and M. Berbineau, editors, Actes INRETS, Communiquer, naviguer, surveiller. Innovations pour des transports plus sûrs, plus efficaces et plus attractifs, volume 112, pages 97–106, Avril 2007.

A. Samé, E. Côme, L. Oukhellou, and P. Aknin. Un algorithme GEM pour le débruitage de signaux. In XIII Rencontres de la Société Francophone de Classification, SFC, pages 195–199, 2006.

E. Côme, A. Samé, L. Oukhellou, and P. Aknin. Régression et modèle de mélange pour le débruitage de signaux. In 2èmes Rencontres Inter-Associations : La classification et ses applications, Mars 20-21, Lyon, 2006.


Thesis

Etienne Côme Docteur de l'UNIVERSITÉ DE TECHNOLOGIE DE COMPIÈGNE, laboratoire HEUDIASYC. Apprentissage de modèles génératids pour le diagnostic de systèmes complexes avec labellisation douce et contraintes spatiales. Thèse soutenue le 16/01/2009. [pdf]