Nombre de documents

67


Article dans une revue17 documents

  • Jangel José Carlos, Cazorla Miguel, Garcia-Varea Ismael, Martinez Gomez Jesus, Elisa Fromont, et al.. Scene classification based on semantic labeling. Advanced Robotics, Taylor & Francis, 2016, 30 (11-12), pp.758-769. 〈10.1080/01691864.2016.1164621〉. 〈hal-01330461〉
  • Aurélien Bellet, José Bernabeu, Amaury Habrard, Marc Sebban. Learning Discriminative Tree Edit Similarities for Linear Classification - Application to Melody Recognition. Neurocomputing, Elsevier, 2016, 214, pp.155-161. 〈10.1016/j.neucom.2016.06.006〉. 〈hal-01330492〉
  • Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban. A New Boosting Algorithm for Provably Accurate Unsupervised Domain Adaptation. Knowledge and Information Systems (KAIS), Springer, 2015, pp.1. 〈10.1007/s10115-015-0839-2〉. 〈hal-01144896〉
  • Fabrice Cognasse, Aloui Chaker, Kim Anh Nguyen, Hind Hamzeh-Cognasse, Fagan Jocelyne, et al.. Platelet components associated with adverse reactions: predictive value of mitochondrial DNA relative to biological response modifiers. Transfusion, Wiley, 2015. 〈hal-01203813〉
  • Kim Anh Nguyen, Hind Hamzeh-Cognasse, Marc Sebban, Elisa Fromont, Patricia Chavarin, et al.. A Computerized Prediction Model of Hazardous Inflammatory Platelet Transfusion Outcomes. PLoS ONE, Public Library of Science, 2014, pp.9(5): e97082. 〈10.1371/journal.pone.0097082〉. 〈hal-01011939〉
  • Aurélien Bellet, Amaury Habrard, Emilie Morvant, Marc Sebban. Learning A Priori Constrained Weighted Majority Votes. Machine Learning, Springer Verlag, 2014, 97 (1-2), pp.129-154. 〈10.1007/s10994-014-5462-z〉. 〈hal-01009578〉
  • Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban. Iterative Self-labeling Domain Adaptation for Linear Structured Image Classification. International Journal on Artificial Intelligence Tools, World Scientific Publishing, 2013. 〈hal-00869404〉
  • Basura Fernando, Elisa Fromont, Damien Muselet, Marc Sebban. Supervised Learning of Gaussian Mixture Models forVisual Vocabulary Generation. Pattern Recognition, Elsevier, 2012, 45 (2), pp.897-907. 〈10.1016/j.patcog.2011.07.021〉. 〈hal-00617693〉
  • Aurélien Bellet, Amaury Habrard, Marc Sebban. Good edit similarity learning by loss minimization. Machine Learning, Springer Verlag, 2012, pp.5-35. 〈10.1007/s10994-012-5293-8〉. 〈hal-00690240〉
  • Aurélien Bellet, Marc Bernard, Thierry Murgue, Marc Sebban. Learning State Machine-based String Edit Kernels. Pattern Recognition, Elsevier, 2010, 43 (2010), pp.2330-2339. 〈10.1016/j.patcog.2009.12.008〉. 〈hal-00462538〉
  • Jean-Christophe Janodet, Marc Sebban, Henri-Maxime Suchier. Boosting Classifiers built from Different Subsets of Features. Fundamenta Informaticae, Polskie Towarzystwo Matematyczne, 2009, 94 (2009), pp.1-21. 〈10.3233/FI-2009-131〉. 〈hal-00403242〉
  • Stéphanie Jacquemont, François Jacquenet, Marc Sebban. A Lower Bound on the Sample Size needed to perform a Significant Frequent Pattern Mining Task. Pattern Recognition Letters, Elsevier, 2009, 30 (2009), pp.960-967. 〈10.1016/j.patrec.2009.05.002〉. 〈hal-00381667v2〉
  • Stéphanie Jacquemont, François Jacquenet, Marc Sebban. Mining Probabilistic Automata: A Statistical View of Sequential Pattern Mining. Machine Learning, Springer Verlag, 2009, 75 (1), pp.91-127. 〈10.1007/s10994-008-5098-y〉. 〈hal-00347865〉
  • Marc Bernard, Laurent Boyer, Amaury Habrard, Marc Sebban. Learning Probabilistic Models of Tree Edit Distance. Pattern Recognition, Elsevier, 2008, 41 (8), pp.2611-2629. 〈10.1016/j.patcog.2008.01.011〉. 〈hal-00224992〉
  • Jose Oncina, Marc Sebban. Learning Stochastic Edit Distance: application in handwritten character recognition. Pattern Recognition, Elsevier, 2006, 39, pp.1575-1587. 〈hal-00114106〉
  • Jean-Christophe Janodet, Marc Sebban, Henri-Maxime Suchier, Richard Nock. Adaptation du boosting à l'inférence grammaticale via l'utilisation d'un oracle de confiance. Revue des Sciences et Technologies de l'Information - Série RIA : Revue d'Intelligence Artificielle, Lavoisier, 2005, 19 (4), pp.713-740. 〈ujm-00352794〉
  • Amaury Habrard, Marc Bernard, Marc Sebban. Detecting Irrelevant subtrees to improve probabilistic learning from tree-structured data. Fundamenta Informaticae, Polskie Towarzystwo Matematyczne, 2005, 66 (1,2), pp.103-130. 〈hal-00369445〉

Communication dans un congrès47 documents

  • Jordan Frery, Amaury Habrard, Marc Sebban, Olivier Caelen, Liyun He-Guelton. Efficient top rank optimization with gradient boosting for supervised anomaly detection. ECML PKDD 2017 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2017, Skopje, Macedonia. 2017. 〈hal-01611346v2〉
  • Jordan Frery, Amaury Habrard, Marc Sebban, Olivier Caelen, Liyun He-Guelton. Efficient top rank optimization with gradient boosting for supervised anomaly detection. ECML-PKDD 2017, Sep 2017, Skopje, Macedonia. 〈hal-01613561〉
  • Ievgen Redko, Amaury Habrard, Marc Sebban. Theoretical Analysis of Domain Adaptation with Optimal Transport. ECML PKDD 2017, Sep 2017, Skopje, Macedonia. 〈hal-01613564〉
  • Guillaume Metzler, Xavier Badiche, Brahim Belkasmi, Stéphane Canu, Elisa Fromont, et al.. Apprentissage de sphères maximales d’exclusion avec garanties théoriques. Conférence sur l'Apprentissage Automatique, Jun 2017, Grenoble, France. 2017, 〈http://cap2017.imag.fr〉. 〈hal-01581550〉
  • Valentina Zantedeschi, Rémi Emonet, Marc Sebban. Apprentissage de Combinaisons Convexes de Métriques Locales avec Garanties de Généralisation. CAp2016, Jul 2016, Marseille, France. 〈hal-01359282〉
  • Valentina Zantedeschi, Rémi Emonet, Marc Sebban. Metric Learning as Convex Combinations of Local Models with Generalization Guarantees. CVPR2016, Jun 2016, Las Vegas, United States. CVPR 2016 Proceedings. 〈hal-01323567〉
  • Valentina Zantedeschi, Rémi Emonet, Marc Sebban. beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data. NIPS 2016, Dec 2016, Barcelona, Spain. Advances in Neural Information Processing Systems 29 (NIPS 2016). 〈hal-01359298〉
  • Nicolae Irina, Marc Sebban, Amaury Habrard, Eric Gaussier, Massih-Reza Amini. Algorithmic Robustness for Semi-Supervised (ε, γ, τ )-Good Metric Learning. International Conference on Neural Information Processing ICONIP, Nov 2015, Istanbul, Turkey. pp.10, 2015. 〈hal-01223411〉
  • Imtiaz Masud Ziko, Elisa Fromont, Damien Muselet, Marc Sebban. Supervised Spectral Subspace Clustering for Visual Dictionary Creation in the Context of Image Classification. ACPR 2015: 3rd IAPR Asian Conference on Pattern Recognition, Nov 2015, Kuala Lumpur, Malaysia. IEEE Xplore, 3rd IAPR Asian Conference on Pattern Recognition. 〈hal-01224466v1〉
  • Jose Carlos Rangel, Miguel Cazorla, Ismael García-Varea, Jesús Martínez-Gómez, Elisa Fromont, et al.. Computing Image Descriptors from Annotations Acquired from External Tools. ROBOT 2015: Second Iberian Robotics Conference, Nov 2015, Lisbon, Portugal. Advances in Intelligent Systems and Computing Series. 〈hal-01224441v1〉
  • Nicolae Irina, Gaussier Eric, Amaury Habrard, Marc Sebban. Joint Semi-supervised Similarity Learning for Linear Classification. ECML-PKDD 2015, Sep 2015, Porto, Portugal. 〈10.1007/978-3-319-23528-8 37〉. 〈hal-01204642〉
  • Rahaf Aljundi, Rémi Emonet, Damien Muselet, Marc Sebban. Landmarks-based Kernelized Subspace Alignment for Unsupervised Domain Adaptation. Computer Vision and Pattern Recognition (CVPR'2015), Jun 2015, Boston, United States. 2015. 〈hal-01124975〉
  • Michaël Perrot, Amaury Habrard, Damien Muselet, Marc Sebban. Modeling Perceptual Color Differences by Local Metric Learning. European Conference on Computer Vision, Sep 2014, Zurich, Switzerland. 2014. 〈hal-01009610〉
  • Michaël Perrot, Amaury Habrard, Damien Muselet, Marc Sebban. Modélisation de Distances Couleur Uniformes par Apprentissage de Métriques Locales. CAp'2014 : Conférence d'Apprentissage Automatique, Jul 2014, Saint-Étienne, France. 2014. 〈hal-01016472〉
  • Aurélien Bellet, Amaury Habrard, Emilie Morvant, Marc Sebban. Vote de majorité a priori contraint pour la classification binaire : spécification au cas des plus proches voisins. Conférence sur l'Apprentissage Automatique (CAp), Jul 2013, Lille, France. 2013. 〈hal-00850241〉
  • Basura Fernando, Amaury Habrard, Marc Sebban, Tinne Tuytelaars. Unsupervised Visual Domain Adaptation Using Subspace Alignment. ICCV 2013, Dec 2013, Sydney, Australia. pp.2960-2967, 2013. 〈hal-00869417〉
  • Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban. Boosting for Unsupervised Domain Adaptation. ECML PKDD 2013, Sep 2013, Prague, Czech Republic. pp.433-448, 2013. 〈hal-00869394〉
  • Leonor Becerra-Bonache, Elisa Fromont, Amaury Habrard, Michael Perrot, Marc Sebban. Speeding Up Syntactic Learning Using Contextual Information. International Conference on Grammatical Inference, Sep 2012, United States. 21, pp.49-53, 2012. 〈hal-00717809〉
  • Basura Fernando, Elisa Fromont, Damien Muselet, Marc Sebban. Discriminative Feature Fusion for Image Classification. Computer Vision and Pattern Recognition, Jun 2012, Rhode Island, United States. pp.3434-3441, 2012. 〈hal-00690244〉
  • Marc Bernard, Elisa Fromont, Amaury Habrard, Marc Sebban. Handwritten Digit Recognition using Edit Distance-Based KNN. Teaching Machine Learning Workshop, Jun 2012, Edinburgh, Scotland, United Kingdom. 〈hal-00714509〉
  • Aurélien Bellet, Amaury Habrard, Marc Sebban. Similarity Learning for Provably Accurate Sparse Linear Classification. International Conference on Machine Learning, Jun 2012, United Kingdom. 2012. 〈hal-00708401〉
  • Aurélien Bellet, Amaury Habrard, Marc Sebban. Apprentissage de bonnes similarités pour la classification linéaire parcimonieuse. Laurent Bougrain. Conférence Francophone sur l'Apprentissage Automatique - CAp 2012, May 2012, Nancy, France. 16 p., 2012. 〈hal-00690242〉
  • Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban. Un Cadre Formel de Boosting pour l'Adaptation de Domaine. CAP 2012, May 2012, Nancy, France. 2012. 〈hal-00690243〉
  • Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban. Un Cadre Formel de Boosting pour l'Adaptation de Domaine. Laurent Bougrain. Conférence Francophone sur l'Apprentissage Automatique - CAp 2012, May 2012, Nancy, France. 16 p., 2012, Actes de la Conférence Francophone sur l'Apprentissage Automatique - CAp 2012. 〈hal-00745487〉
  • Aurélien Bellet, Amaury Habrard, Marc Sebban. Learning Good Edit Similarities with Generalization Guarantees. European Conference on Machine Learning, Sep 2011, Athens, Greece. pp.188-203, 2011. 〈hal-00608631〉
  • Marc Bernard, Baptiste Jeudy, Jean-Philippe Peyrache, Marc Sebban, Franck Thollard. Using the H-divergence to Prune Probabilistic Automata. ICTAI 2011, Nov 2011, Boca Raton, United States. 2011. 〈hal-00618713〉
  • Aurélien Bellet, Amaury Habrard, Marc Sebban. An Experimental Study on Learning with Good Edit Similarity Functions. ICTAI 2011, Nov 2011, United States. 2011. 〈hal-00618706〉
  • Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban. Domain Adaptation with Good Edit Similarities: a Sparse Way to deal with Scaling and Rotation Problems in Image Classification. ICTAI 2011, Nov 2011, United States. 2011. 〈hal-00618757〉
  • Basura Fernando, Elisa Fromont, Damien Muselet, Marc Sebban. Accurate Visual Word Construction using a Supervised Approach. 25th International Conference of Image and Vision Computing New Zealand, Nov 2010, New Zealand. 2010. 〈hal-00555639〉
  • Cécile Barat, Christophe Ducottet, Elisa Fromont, Anne-Claire Legrand, Marc Sebban. Weighted Symbols-based Edit Distance for String-Structured Image Classification. ECML PKDD 2010 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases., Sep 2010, Barcelona, Spain. 2010. 〈hal-00499468〉
  • Laurent Boyer, Olivier Gandrillon, Amaury Habrard, Mathilde Pellerin, Marc Sebban. Learning Constrained Edit State Machines. 21st IEEE International Conference on Tools with Artificial Intelligence, Nov 2009, United States. pp.734-741, 2009. 〈hal-00485560〉
  • Stéphanie Jacquemont, François Jacquenet, Marc Sebban. Discovering Patterns in Flows: a Privacy Preserving Approach with the ACSM Prototype. Wray Buntine and Marko Grobelnik and Dunja Mladeni\' and John Shawe-Taylor. ECML PKDD, Sep 2009, Bled, Slovenia. Springer, 5782, pp.734--737, 2009, Lecture Notes in Computer Science. 〈hal-00431774〉
  • Aurélien Bellet, Marc Bernard, Thierry Murgue, Marc Sebban. Apprentissage de noyaux d'édition de séquences. Conférence d'Apprentissage : CAP 2009, May 2009, Hammamet, Tunisia. pp.à venir, 2009. 〈ujm-00378078〉
  • Laurent Boyer, Yann Esposito, Amaury Habrard, Jose Oncina, Marc Sebban. SEDiL: Software for Edit Distance Learning. European Conference on Machine Learning (ECML 2008), Sep 2008, Belgium. Springer, pp.672-677, 2008, Lecture Notes in Computer Science. 〈hal-00295148〉
  • Amaury Habrard, Jose-Manuel Inesta, David Rizo, Marc Sebban. Melody Recognition with Learned Edit Distances. Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshops, SSPR 2008 and SPR 2008, Dec 2008, Orlando, United States. Springer, pp.86-96, 2008, Lecture Notes in Computer Science. 〈hal-00322432〉
  • Laurent Boyer, Amaury Habrard, Fabrice Muhlenbach, Marc Sebban. Learning String Edit Similarities using Constrained Finite State Machines. CAp'08, May 2008, Porquerolles, France. pp.37-52, 2008. 〈hal-00369444〉
  • Stéphanie Jacquemont, François Jacquenet, Marc Sebban. Correct your Text with Google. IEEE International Conference on Web Intelligence, Nov 2007, Fremont, United States. pp.xx-xx, 2007. 〈hal-00175284〉
  • Laurent Boyer, Amaury Habrard, Marc Sebban. Learning Metrics between Tree Structured Data: Application to Image Recognition. 18th European Conference on Machine Learning (ECML), Sep 2007, Warsaw, Poland. Springer, LNAI 4701, pp.54-66, 2007, Lecture Notes in Computer Science. 〈hal-00165954〉
  • Marc Bernard, Jean-Christophe Janodet, Marc Sebban. Learning Conditional Transducers for Estimating the Distribution of String Edit Costs. Grammatical Inference: workshop on open problems and new directions, May 2006, Saint-Etienne, France. 2006. 〈ujm-00352796〉
  • Stéphanie Jacquemont, François Jacquenet, Marc Sebban. Sequence Mining Without Sequences: a New Way for Privacy Preserving. IEEE Computer Society. 2006, IEEE Computer Society, pp.347-354, 2006. 〈hal-00114132〉
  • Jose Oncina, Marc Sebban. Using Learned Conditional Distributions as Edit Distance. Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshops, SSPR 2006 and SPR 2006, Aug 2006, Hong Kong, China. Springer, 4109, pp 403-411, ISBN 3-540-37236-9, 2006, Lecture Notes in Computer Science. 〈hal-00322429〉
  • Jean-Christophe Janodet, Henri-Maxime Suchier, Marc Sebban, Christine Largeron. Boosting d'un pool d'apprenants faibles. CAp 2006, May 2006, Trégastel, France. Presses Universitaires de Grenoble, pp.283-298, 2006. 〈hal-00114532〉
  • Marc Bernard, Amaury Habrard, Marc Sebban. Learning Stochastic Tree Edit Distance. 17th European Conference on Machine Learning, Sep 2006, Berlin, Germany. pp.42-53, 2006, LNAI 4212. 〈ujm-00109696〉
  • Marc Bernard, Jean-Christophe Janodet, Marc Sebban. A Discriminative Model of Stochastic Edit Distance in the form of a Conditional Transducer. 8th International Colloquium on Grammatical Inference, Sep 2006, Tokyo, Japan. pp.240-252, 2006, LNAI 4201. 〈ujm-00109698〉
  • Stéphanie Jacquemont, François Jacquenet, Marc Sebban. Constrained Sequence Mining based on Probabilistic Finite State Automata. Workshop on Mining Graphs, Trees and Structured Data at ECML/PKDD, Oct 2005, Portugal. 〈hal-00374061〉
  • Stéphanie Jacquemont, François Jacquenet, Marc Sebban. Constrained Sequence Mining based on Probabilistic Finite State Automata. Conférence d'Apprentissage Automatique, 2005, France. pp.15-30, 2005. 〈hal-00374062〉
  • Amaury Habrard, Marc Bernard, Marc Sebban. Correction of Uniformly Noisy Distributions to Improve Probabilistic Grammatical Inference Algorithms. 18th International Florida Artificial Intelligence Research Society conference, May 2005, United States. pp.493-498, 2005. 〈ujm-00378062〉

Ouvrage (y compris édition critique et traduction)2 documents

Chapitre d'ouvrage1 document

  • Basura Fernando, Rahaf Aljundi, Rémi Emonet, Amaury Habrard, Marc Sebban, et al.. Unsupervised Domain Adaptation Based on Subspace Alignment. Domain Adaptation in Computer Vision Applications. Advances in Computer Vision and Pattern Recognition, 2017. 〈hal-01613051〉