Marc Sebban

According to our database, Marc Sebban authored at least 118 papers between 1996 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Approximation Error of Sobolev Regular Functions with Tanh Neural Networks: Theoretical Impact on PINNs.

Benjamin Girault, Rémi Emonet, Amaury Habrard, Jordan Patracone, Marc Sebban

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024

Generative shape deformation with optimal transport using learned transformations.

Jorge Azorín López, Marc Sebban, Nahuel E. Garcia-D'Urso, Amaury Habrard, Andrés Fuster Guilló

Proceedings of the International Joint Conference on Neural Networks, 2024

2023

Predictive Modeling of Body Shape Changes in Individuals on Dietetic Treatment Using Recurrent Networks.

Nahuel E. Garcia-D'Urso, Pablo Ramon Guevara, Jorge Azorín López, Marc Sebban, Amaury Habrard, Andrés Fuster Guilló

Proceedings of the 15th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2023), 2023

Is My Neural Net Driven by the MDL Principle?

Eduardo Brandao, Stefan Duffner, Rémi Emonet, Amaury Habrard, François Jacquenet, Marc Sebban

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

2022

MetaAP: A meta-tree-based ranking algorithm optimizing the average precision from imbalanced data.

Rémi Viola, Léo Gautheron, Amaury Habrard, Marc Sebban

Pattern Recognit. Lett., 2022

Learning PDE to Model Self-Organization of Matter.

Eduardo Brandao, Jean-Philippe Colombier, Stefan Duffner, Rémi Emonet, Florence Garrelie, Amaury Habrard, François Jacquenet, Anthony Nakhoul, Marc Sebban

Entropy, 2022

Architecture for Automatic Recognition of Group Activities Using Local Motions and Context.

Luis Felipe Borja-Borja, Jorge Azorín López, Marcelo Saval-Calvo, Andrés Fuster Guilló, Marc Sebban

IEEE Access, 2022

Fast Multiscale Diffusion On Graphs.

Sibylle Marcotte, Amélie Barbe, Rémi Gribonval, Titouan Vayer, Marc Sebban, Pierre Borgnat, Paulo Gonçalves

Proceedings of the IEEE International Conference on Acoustics, 2022

Why do State-of-the-art Super-Resolution Methods not work well for Bone Microstructure CT Imaging?

Rehan Jhuboo, Ievgen Redko, Alain Guignandon, Françoise Peyrin, Marc Sebban

Proceedings of the 30th European Signal Processing Conference, 2022

Optimal Tensor Transport.

Tanguy Kerdoncuff, Rémi Emonet, Michaël Perrot, Marc Sebban

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Iterative multilinear optimization for planar model fitting under geometric constraints.

Jorge Azorín López, Marc Sebban, Andrés Fuster Guilló, Marcelo Saval-Calvo, Amaury Habrard

PeerJ Comput. Sci., 2021

Sampled Gromov Wasserstein.

Tanguy Kerdoncuff, Rémi Emonet, Marc Sebban

Mach. Learn., 2021

A Nearest Neighbor Algorithm for Imbalanced Classification.

Rémi Viola, Rémi Emonet, Amaury Habrard, Guillaume Metzler, Sébastien Riou, Marc Sebban

Int. J. Artif. Intell. Tools, 2021

Optimization of the Diffusion Time in Graph Diffused-Wasserstein Distances: Application to Domain Adaptation.

Amélie Barbe, Paulo Gonçalves, Marc Sebban, Pierre Borgnat, Rémi Gribonval, Titouan Vayer

Proceedings of the 33rd IEEE International Conference on Tools with Artificial Intelligence, 2021

2020

Metric Learning from Imbalanced Data with Generalization Guarantees.

Léo Gautheron, Amaury Habrard, Emilie Morvant, Marc Sebban

Pattern Recognit. Lett., 2020

A survey on domain adaptation theory.

Ievgen Redko, Emilie Morvant, Amaury Habrard, Marc Sebban, Younès Bennani

CoRR, 2020

Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting.

Léo Gautheron, Pascal Germain, Amaury Habrard, Guillaume Metzler, Emilie Morvant, Marc Sebban, Valentina Zantedeschi

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

Graph Diffusion Wasserstein Distances.

Amélie Barbe, Marc Sebban, Paulo Gonçalves, Pierre Borgnat, Rémi Gribonval

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

Learning from Few Positives: a Provably Accurate Metric Learning Algorithm to Deal with Imbalanced Data.

Rémi Viola, Rémi Emonet, Amaury Habrard, Guillaume Metzler, Marc Sebban

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Metric Learning in Optimal Transport for Domain Adaptation.

Tanguy Kerdoncuff, Rémi Emonet, Marc Sebban

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

A Swiss Army Knife for Minimax Optimal Transport.

Sofien Dhouib, Ievgen Redko, Tanguy Kerdoncuff, Rémi Emonet, Marc Sebban

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Deep multi-Wasserstein unsupervised domain adaptation.

Tien-Nam Le, Amaury Habrard, Marc Sebban

Pattern Recognit. Lett., 2019

On the analysis of adaptability in multi-source domain adaptation.

Ievgen Redko, Amaury Habrard, Marc Sebban

Mach. Learn., 2019

Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting.

Léo Gautheron, Pascal Germain, Amaury Habrard, Emilie Morvant, Marc Sebban, Valentina Zantedeschi

CoRR, 2019

Differentially Private Optimal Transport: Application to Domain Adaptation.

Nam Lê Tien, Amaury Habrard, Marc Sebban

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data.

Rémi Viola, Rémi Emonet, Amaury Habrard, Guillaume Metzler, Sébastien Riou, Marc Sebban

Proceedings of the 31st IEEE International Conference on Tools with Artificial Intelligence, 2019

Metric Learning from Imbalanced Data.

Léo Gautheron, Amaury Habrard, Emilie Morvant, Marc Sebban

Proceedings of the 31st IEEE International Conference on Tools with Artificial Intelligence, 2019

From Cost-Sensitive to Tight F-measure Bounds.

Kevin Bascol, Rémi Emonet, Élisa Fromont, Amaury Habrard, Guillaume Metzler, Marc Sebban

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Learning maximum excluding ellipsoids from imbalanced data with theoretical guarantees.

Guillaume Metzler, Xavier Badiche, Brahim Belkasmi, Élisa Fromont, Amaury Habrard, Marc Sebban

Pattern Recognit. Lett., 2018

Fast and Provably Effective Multi-view Classification with Landmark-Based SVM.

Valentina Zantedeschi, Rémi Emonet, Marc Sebban

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

Non-Linear Gradient Boosting for Class-Imbalance Learning.

Jordan Fréry, Amaury Habrard, Marc Sebban, Liyun He-Guelton

Proceedings of the Second International Workshop on Learning with Imbalanced Domains: Theory and Applications, 2018

Tree-Based Cost Sensitive Methods for Fraud Detection in Imbalanced Data.

Guillaume Metzler, Xavier Badiche, Brahim Belkasmi, Élisa Fromont, Amaury Habrard, Marc Sebban

Proceedings of the Advances in Intelligent Data Analysis XVII, 2018

Online Non-linear Gradient Boosting in Multi-latent Spaces.

Jordan Fréry, Amaury Habrard, Marc Sebban, Olivier Caelen, Liyun He-Guelton

Proceedings of the Advances in Intelligent Data Analysis XVII, 2018

2017

Unsupervised Domain Adaptation Based on Subspace Alignment.

Basura Fernando, Rahaf Aljundi, Rémi Emonet, Amaury Habrard, Marc Sebban, Tinne Tuytelaars

Proceedings of the Domain Adaptation in Computer Vision Applications., 2017

L³-SVMs: Landmarks-based Linear Local Support Vectors Machines.

Valentina Zantedeschi, Rémi Emonet, Marc Sebban

CoRR, 2017

Theoretical Analysis of Domain Adaptation with Optimal Transport.

Ievgen Redko, Amaury Habrard, Marc Sebban

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Efficient Top Rank Optimization with Gradient Boosting for Supervised Anomaly Detection.

Jordan Fréry, Amaury Habrard, Marc Sebban, Olivier Caelen, Liyun He-Guelton

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

2016

A new boosting algorithm for provably accurate unsupervised domain adaptation.

Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban

Knowl. Inf. Syst., 2016

Learning discriminative tree edit similarities for linear classification - Application to melody recognition.

Aurélien Bellet, José Francisco Bernabeu, Amaury Habrard, Marc Sebban

Neurocomputing, 2016

Lipschitz Continuity of Mahalanobis Distances and Bilinear Forms.

Valentina Zantedeschi, Rémi Emonet, Marc Sebban

CoRR, 2016

Similarity Learning for Time Series Classification.

Maria-Irina Nicolae, Éric Gaussier, Amaury Habrard, Marc Sebban

CoRR, 2016

Scene classification based on semantic labeling.

José Carlos Rangel, Miguel Cazorla, Ismael García-Varea, Jesus Martínez-Gómez, Élisa Fromont, Marc Sebban

Adv. Robotics, 2016

beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data.

Valentina Zantedeschi, Rémi Emonet, Marc Sebban

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Metric Learning as Convex Combinations of Local Models with Generalization Guarantees.

Valentina Zantedeschi, Rémi Emonet, Marc Sebban

Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

2015

Metric Learning

Aurélien Bellet, Amaury Habrard, Marc Sebban

Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, ISBN: 978-3-031-01572-4, 2015

Algorithmic Robustness for Semi-Supervised (ε, γ, τ)-Good Metric Learning.

Maria-Irina Nicolae, Marc Sebban, Amaury Habrard, Éric Gaussier, Massih-Reza Amini

Proceedings of the 3rd International Conference on Learning Representations, 2015

Computing Image Descriptors from Annotations Acquired from External Tools.

José Carlos Rangel, Miguel Cazorla, Ismael García-Varea, Jesus Martínez-Gómez, Élisa Fromont, Marc Sebban

Proceedings of the Robot 2015: Second Iberian Robotics Conference, 2015

Joint Semi-supervised Similarity Learning for Linear Classification.

Maria-Irina Nicolae, Éric Gaussier, Amaury Habrard, Marc Sebban

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Landmarks-based kernelized subspace alignment for unsupervised domain adaptation.

Rahaf Aljundi, Rémi Emonet, Damien Muselet, Marc Sebban

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

Supervised spectral subspace clustering for visual dictionary creation in the context of image classification.

Imtiaz Masud Ziko, Élisa Fromont, Damien Muselet, Marc Sebban

Proceedings of the 3rd IAPR Asian Conference on Pattern Recognition, 2015

2014

Learning a priori constrained weighted majority votes.

Aurélien Bellet, Amaury Habrard, Emilie Morvant, Marc Sebban

Mach. Learn., 2014

Subspace Alignment For Domain Adaptation.

Basura Fernando, Amaury Habrard, Marc Sebban, Tinne Tuytelaars

CoRR, 2014

Modeling Perceptual Color Differences by Local Metric Learning.

Michaël Perrot, Amaury Habrard, Damien Muselet, Marc Sebban

Proceedings of the Computer Vision - ECCV 2014, 2014

2013

Iterative Self-Labeling Domain Adaptation for Linear Structured Image Classification.

Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban

Int. J. Artif. Intell. Tools, 2013

Combining Feature and Prototype Pruning by Uncertainty Minimization

Marc Sebban, Richard Nock

CoRR, 2013

A Survey on Metric Learning for Feature Vectors and Structured Data.

Aurélien Bellet, Amaury Habrard, Marc Sebban

CoRR, 2013

Boosting for Unsupervised Domain Adaptation.

Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

Unsupervised Visual Domain Adaptation Using Subspace Alignment.

Basura Fernando, Amaury Habrard, Marc Sebban, Tinne Tuytelaars

Proceedings of the IEEE International Conference on Computer Vision, 2013

2012

Supervised learning of Gaussian mixture models for visual vocabulary generation.

Basura Fernando, Élisa Fromont, Damien Muselet, Marc Sebban

Pattern Recognit., 2012

Good edit similarity learning by loss minimization.

Aurélien Bellet, Amaury Habrard, Marc Sebban

Mach. Learn., 2012

Speeding Up Syntactic Learning Using Contextual Information.

Leonor Becerra-Bonache, Élisa Fromont, Amaury Habrard, Michaël Perrot, Marc Sebban

Proceedings of the Eleventh International Conference on Grammatical Inference, 2012

Similarity Learning for Provably Accurate Sparse Linear Classification.

Aurélien Bellet, Amaury Habrard, Marc Sebban

Proceedings of the 29th International Conference on Machine Learning, 2012

Discriminative feature fusion for image classification.

Basura Fernando, Élisa Fromont, Damien Muselet, Marc Sebban

Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

2011

Learning Good Edit Similarities with Generalization Guarantees.

Aurélien Bellet, Amaury Habrard, Marc Sebban

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011

Domain Adaptation with Good Edit Similarities: A Sparse Way to Deal with Scaling and Rotation Problems in Image Classification.

Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban

Proceedings of the IEEE 23rd International Conference on Tools with Artificial Intelligence, 2011

Using the H-Divergence to Prune Probabilistic Automata.

Marc Bernard, Baptiste Jeudy, Jean-Philippe Peyrache, Marc Sebban, Franck Thollard

Proceedings of the IEEE 23rd International Conference on Tools with Artificial Intelligence, 2011

An Experimental Study on Learning with Good Edit Similarity Functions.

Aurélien Bellet, Marc Sebban, Amaury Habrard

Proceedings of the IEEE 23rd International Conference on Tools with Artificial Intelligence, 2011

2010

Learning state machine-based string edit kernels.

Aurélien Bellet, Marc Bernard, Thierry Murgue, Marc Sebban

Pattern Recognit., 2010

Weighted Symbols-Based Edit Distance for String-Structured Image Classification.

Cécile Barat, Christophe Ducottet, Élisa Fromont, Anne-Claire Legrand, Marc Sebban

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

2009

A lower bound on the sample size needed to perform a significant frequent pattern mining task.

Stéphanie Jacquemont, François Jacquenet, Marc Sebban

Pattern Recognit. Lett., 2009

Mining probabilistic automata: a statistical view of sequential pattern mining.

Stéphanie Jacquemont, François Jacquenet, Marc Sebban

Mach. Learn., 2009

Boosting Classifiers Built from Different Subsets of Features.

Jean-Christophe Janodet, Marc Sebban, Henri-Maxime Suchier

Fundam. Informaticae, 2009

Discovering Patterns in Flows: A Privacy Preserving Approach with the ACSM Prototype.

Stéphanie Jacquemont, François Jacquenet, Marc Sebban

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2009

Learning Constrained Edit State Machines.

Laurent Boyer, Olivier Gandrillon, Amaury Habrard, Mathilde Pellerin, Marc Sebban

Proceedings of the ICTAI 2009, 2009

2008

Learning probabilistic models of tree edit distance.

Marc Bernard, Laurent Boyer, Amaury Habrard, Marc Sebban

Pattern Recognit., 2008

Melody Recognition with Learned Edit Distances.

Amaury Habrard, José Manuel Iñesta Quereda, David Rizo, Marc Sebban

Proceedings of the Structural, 2008

SEDiL: Software for Edit Distance Learning.

Laurent Boyer, Yann Esposito, Amaury Habrard, José Oncina, Marc Sebban

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2008

2007

Correct your text with Google.

Stéphanie Jacquemont, François Jacquenet, Marc Sebban

Proceedings of the 2007 IEEE / WIC / ACM International Conference on Web Intelligence, 2007

Learning Metrics Between Tree Structured Data: Application to Image Recognition.

Laurent Boyer, Amaury Habrard, Marc Sebban

Proceedings of the Machine Learning: ECML 2007, 2007

2006

Learning stochastic edit distance: Application in handwritten character recognition.

José Oncina, Marc Sebban

Pattern Recognit., 2006

Using Learned Conditional Distributions as Edit Distance.

José Oncina, Marc Sebban

Proceedings of the Structural, 2006

Sequence Mining Without Sequences: A New Way for Privacy Preserving.

Stéphanie Jacquemont, François Jacquenet, Marc Sebban

Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2006), 2006

A Discriminative Model of Stochastic Edit Distance in the Form of a Conditional Transducer.

Marc Bernard, Jean-Christophe Janodet, Marc Sebban

Proceedings of the Grammatical Inference: Algorithms and Applications, 2006

Learning Stochastic Tree Edit Distance.

Marc Bernard, Amaury Habrard, Marc Sebban

Proceedings of the Machine Learning: ECML 2006, 2006

2005

Adaptation du boosting à l'inférence grammaticale <i>via</i> l'utilisation d'un oracle de confiance.

Jean-Christophe Janodet, Richard Nock, Marc Sebban, Henri-Maxime Suchier

Rev. d'Intelligence Artif., 2005

Detecting Irrelevant Subtrees to Improve Probabilistic Learning from Tree-structured Data.

Amaury Habrard, Marc Bernard, Marc Sebban

Fundam. Informaticae, 2005

Correction of Uniformly Noisy Distributions to Improve Probabilistic Grammatical Inference Algorithms.

Amaury Habrard, Marc Bernard, Marc Sebban

Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, 2005

Constrained Sequence Mining based on Probabilistic Finite State Automata.

Stéphanie Jacquemont, François Jacquenet, Marc Sebban

Proceedings of the Actes de CAP 05, Conférence francophone sur l'apprentissage automatique, 2005

2004

Mining Decision Rules from Deterministic Finite Automata.

François Jacquenet, Marc Sebban, Georges Valétudie

Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004), 2004

Boosting grammatical inference with confidence oracles.

Jean-Christophe Janodet, Richard Nock, Marc Sebban, Henri-Maxime Suchier

Proceedings of the Machine Learning, 2004

2003

A Simple Locally Adaptive Nearest Neighbor Rule With Application To Pollution Forecasting.

Richard Nock, Marc Sebban, Didier Bernard

Int. J. Pattern Recognit. Artif. Intell., 2003

On State Merging in Grammatical Inference: A Statistical Approach for Dealing with Noisy Data.

Marc Sebban, Jean-Christophe Janodet

Proceedings of the Machine Learning, 2003

On Boosting Improvement: Error Reduction and Convergence Speed-Up.

Marc Sebban, Henri-Maxime Suchier

Proceedings of the Machine Learning: ECML 2003, 2003

Improvement of the State Merging Rule on Noisy Data in Probabilistic Grammatical Inference.

Amaury Habrard, Marc Bernard, Marc Sebban

Proceedings of the Machine Learning: ECML 2003, 2003

2002

A hybrid filter/wrapper approach of feature selection using information theory.

Marc Sebban, Richard Nock

Pattern Recognit., 2002

Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem.

Marc Sebban, Richard Nock, Stéphane Lallich

J. Mach. Learn. Res., 2002

A data-mining approach to spacer oligonucleotide typing of Mycobacterium tuberculosis.

Marc Sebban, Igor Mokrousov, Nalin Rastogi, Christophe Sola

Bioinform., 2002

Boosting Density Function Estimators.

Franck Thollard, Marc Sebban, Philippe Ézéquel

Proceedings of the Machine Learning: ECML 2002, 2002

2001

A Bayesian boosting theorem.

Richard Nock, Marc Sebban

Pattern Recognit. Lett., 2001

An improved bound on the finite-sample risk of the nearest neighbor rule.

Richard Nock, Marc Sebban

Pattern Recognit. Lett., 2001

Advances in Adaptive Prototype Weighting and Selection.

Richard Nock, Marc Sebban

Int. J. Artif. Intell. Tools, 2001

Boosting Neighborhood-Based Classifiers.

Marc Sebban, Richard Nock, Stéphane Lallich

Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28, 2001

Improvement of Nearest-Neighbor Classifiers via Support Vector Machines.

Marc Sebban, Richard Nock

Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference, 2001

2000

Impact of learning set quality and size on decision tree performances.

Marc Sebban, Richard Nock, Jean-Hugues Chauchat, Ricco Rakotomalala

Int. J. Comput. Syst. Signals, 2000

Combining Feature and Example Pruning by Uncertainty Minimization.

Marc Sebban, Richard Nock

Proceedings of the UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30, 2000

Contribution of Dataset Reduction Techniques to Tree-Simplification and Knowledge Discovery.

Marc Sebban, Richard Nock

Proceedings of the Principles of Data Mining and Knowledge Discovery, 2000

Instance Pruning as an Information Preserving Problem.

Marc Sebban, Richard Nock

Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29, 2000

A Boosting-Based Prototype Weighting and Selection Scheme.

Richard Nock, Marc Sebban

Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference, 2000

A Symmetric Nearest Neighbor Learning Rule.

Richard Nock, Marc Sebban, Pascal Jabby

Proceedings of the Advances in Case-Based Reasoning, 5th European Workshop, 2000

Sharper Bounds for the Hardness of Prototype and Feature Selection.

Richard Nock, Marc Sebban

Proceedings of the Algorithmic Learning Theory, 11th International Conference, 2000

Identifying and Eliminating Irrelevant Instances Using Information Theory.

Marc Sebban, Richard Nock

Proceedings of the Advances in Artificial Intelligence, 2000

1999

Selection and Statistical Validation of Features and Prototypes.

Marc Sebban, Djamel A. Zighed, S. Di Palma

Proceedings of the Principles of Data Mining and Knowledge Discovery, 1999

Contribution of Boosting in Wrapper Models.

Marc Sebban, Richard Nock

Proceedings of the Principles of Data Mining and Knowledge Discovery, 1999

Experiments on a Representation-Independent "Top-Down and Prune" Induction Scheme.

Richard Nock, Marc Sebban, Pascal Jappy

Proceedings of the Principles of Data Mining and Knowledge Discovery, 1999

From Theoretical Learnability to Statistical Measures of the Learnable.

Marc Sebban, Gilles Richard

Proceedings of the Advances in Intelligent Data Analysis, Third International Symposium, 1999

1998

Prototype Selection from Homogeneous Subsets by a Monte Carlo Sampling.

Marc Sebban

Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference, 1998

Strings Clustering and Statistical Validation of Clusters.

Marc Sebban, Anne M. Landraud

Proceedings of the Advances in Artificial Intelligence, 1998

1996

A Comparison of Some Contextual Discretization Methods.

Sabine Rabaséda, Ricco Rakotomalala, Marc Sebban

Inf. Sci., 1996