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2020 – today
- 2024
[c75]Jorge Azorín López, Marc Sebban, Nahuel E. Garcia-D'Urso, Amaury Habrard, Andrés Fuster Guilló:
Generative shape deformation with optimal transport using learned transformations. IJCNN 2024: 1-8- [c74]Benjamin Girault, Rémi Emonet, Amaury Habrard, Jordan Patracone, Marc Sebban:
Approximation Error of Sobolev Regular Functions with Tanh Neural Networks: Theoretical Impact on PINNs. ECML/PKDD (4) 2024: 266-282 - 2023
- [c73]Eduardo Brandao
, Stefan Duffner, Rémi Emonet, Amaury Habrard, François Jacquenet, Marc Sebban:
Is My Neural Net Driven by the MDL Principle? ECML/PKDD (2) 2023: 173-189 - [c72]Nahuel E. Garcia-D'Urso, Pablo Ramon Guevara, Jorge Azorín López, Marc Sebban, Amaury Habrard, Andrés Fuster Guilló:
Predictive Modeling of Body Shape Changes in Individuals on Dietetic Treatment Using Recurrent Networks. UCAmI (2) 2023: 100-111 - 2022
- [j34]Luis Felipe Borja-Borja, Jorge Azorín López, Marcelo Saval-Calvo, Andrés Fuster Guilló, Marc Sebban:
Architecture for Automatic Recognition of Group Activities Using Local Motions and Context. IEEE Access 10: 79874-79889 (2022) - [j33]Eduardo Brandao, Jean-Philippe Colombier, Stefan Duffner, Rémi Emonet, Florence Garrelie, Amaury Habrard, François Jacquenet, Anthony Nakhoul, Marc Sebban:
Learning PDE to Model Self-Organization of Matter. Entropy 24(8): 1096 (2022) - [j32]Rémi Viola, Léo Gautheron, Amaury Habrard, Marc Sebban:
MetaAP: A meta-tree-based ranking algorithm optimizing the average precision from imbalanced data. Pattern Recognit. Lett. 161: 161-167 (2022) - [c71]Tanguy Kerdoncuff, Rémi Emonet, Michaël Perrot, Marc Sebban:
Optimal Tensor Transport. AAAI 2022: 7124-7132 - [c70]Rehan Jhuboo, Ievgen Redko, Alain Guignandon, Françoise Peyrin, Marc Sebban:
Why do State-of-the-art Super-Resolution Methods not work well for Bone Microstructure CT Imaging? EUSIPCO 2022: 1283-1287 - [c69]Sibylle Marcotte, Amélie Barbe, Rémi Gribonval, Titouan Vayer, Marc Sebban, Pierre Borgnat, Paulo Gonçalves:
Fast Multiscale Diffusion On Graphs. ICASSP 2022: 5627-5631 - 2021
- [j31]Rémi Viola, Rémi Emonet, Amaury Habrard, Guillaume Metzler, Sébastien Riou, Marc Sebban:
A Nearest Neighbor Algorithm for Imbalanced Classification. Int. J. Artif. Intell. Tools 30(3): 2150013:1-2150013:27 (2021) - [j30]Tanguy Kerdoncuff, Rémi Emonet, Marc Sebban:
Sampled Gromov Wasserstein. Mach. Learn. 110(8): 2151-2186 (2021) - [j29]Jorge Azorín López, Marc Sebban, Andrés Fuster Guilló, Marcelo Saval-Calvo, Amaury Habrard:
Iterative multilinear optimization for planar model fitting under geometric constraints. PeerJ Comput. Sci. 7: e691 (2021) - [c68]Amélie Barbe, Paulo Gonçalves, Marc Sebban, Pierre Borgnat, Rémi Gribonval, Titouan Vayer:
Optimization of the Diffusion Time in Graph Diffused-Wasserstein Distances: Application to Domain Adaptation. ICTAI 2021: 786-790 - [i12]Sibylle Marcotte, Amélie Barbe, Rémi Gribonval, Titouan Vayer, Marc Sebban, Pierre Borgnat, Paulo Gonçalves:
Fast Multiscale Diffusion on Graphs. CoRR abs/2104.14652 (2021) - 2020
- [j28]Léo Gautheron, Amaury Habrard, Emilie Morvant, Marc Sebban:
Metric Learning from Imbalanced Data with Generalization Guarantees. Pattern Recognit. Lett. 133: 298-304 (2020) - [c67]Sofien Dhouib, Ievgen Redko, Tanguy Kerdoncuff, Rémi Emonet, Marc Sebban:
A Swiss Army Knife for Minimax Optimal Transport. ICML 2020: 2504-2513 - [c66]Rémi Viola, Rémi Emonet, Amaury Habrard, Guillaume Metzler, Marc Sebban:
Learning from Few Positives: a Provably Accurate Metric Learning Algorithm to Deal with Imbalanced Data. IJCAI 2020: 2155-2161 - [c65]Tanguy Kerdoncuff, Rémi Emonet, Marc Sebban:
Metric Learning in Optimal Transport for Domain Adaptation. IJCAI 2020: 2162-2168 - [c64]Léo Gautheron, Pascal Germain, Amaury Habrard, Guillaume Metzler, Emilie Morvant, Marc Sebban, Valentina Zantedeschi:
Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting. ECML/PKDD (3) 2020: 141-157 - [c63]Amélie Barbe, Marc Sebban, Paulo Gonçalves, Pierre Borgnat, Rémi Gribonval:
Graph Diffusion Wasserstein Distances. ECML/PKDD (2) 2020: 577-592 - [i11]Ievgen Redko, Emilie Morvant, Amaury Habrard, Marc Sebban, Younès Bennani:
A survey on domain adaptation theory. CoRR abs/2004.11829 (2020)
2010 – 2019
- 2019
- [j27]Ievgen Redko, Amaury Habrard, Marc Sebban:
On the analysis of adaptability in multi-source domain adaptation. Mach. Learn. 108(8-9): 1635-1652 (2019) - [j26]Tien-Nam Le, Amaury Habrard, Marc Sebban:
Deep multi-Wasserstein unsupervised domain adaptation. Pattern Recognit. Lett. 125: 249-255 (2019) - [c62]Kevin Bascol, Rémi Emonet, Élisa Fromont, Amaury Habrard, Guillaume Metzler, Marc Sebban:
From Cost-Sensitive to Tight F-measure Bounds. AISTATS 2019: 1245-1253 - [c61]Rémi Viola, Rémi Emonet, Amaury Habrard, Guillaume Metzler, Sébastien Riou, Marc Sebban:
An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data. ICTAI 2019: 243-250 - [c60]Léo Gautheron, Amaury Habrard, Emilie Morvant, Marc Sebban:
Metric Learning from Imbalanced Data. ICTAI 2019: 923-930 - [c59]Nam Lê Tien, Amaury Habrard, Marc Sebban:
Differentially Private Optimal Transport: Application to Domain Adaptation. IJCAI 2019: 2852-2858 - [i10]Léo Gautheron, Pascal Germain, Amaury Habrard, Emilie Morvant, Marc Sebban, Valentina Zantedeschi:
Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting. CoRR abs/1906.06203 (2019) - [i9]Rémi Viola, Rémi Emonet, Amaury Habrard, Guillaume Metzler, Sébastien Riou, Marc Sebban:
An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data. CoRR abs/1909.00693 (2019) - [i8]Léo Gautheron, Emilie Morvant, Amaury Habrard, Marc Sebban:
Metric Learning from Imbalanced Data. CoRR abs/1909.01651 (2019) - 2018
- [j25]Guillaume Metzler, Xavier Badiche, Brahim Belkasmi, Élisa Fromont, Amaury Habrard, Marc Sebban:
Learning maximum excluding ellipsoids from imbalanced data with theoretical guarantees. Pattern Recognit. Lett. 112: 310-316 (2018) - [c58]Jordan Fréry, Amaury Habrard, Marc Sebban, Olivier Caelen, Liyun He-Guelton:
Online Non-linear Gradient Boosting in Multi-latent Spaces. IDA 2018: 99-110 - [c57]Guillaume Metzler, Xavier Badiche, Brahim Belkasmi, Élisa Fromont, Amaury Habrard, Marc Sebban:
Tree-Based Cost Sensitive Methods for Fraud Detection in Imbalanced Data. IDA 2018: 213-224 - [c56]Jordan Fréry, Amaury Habrard, Marc Sebban, Liyun He-Guelton:
Non-Linear Gradient Boosting for Class-Imbalance Learning. LIDTA@ECML/PKDD 2018: 38-51 - [c55]Valentina Zantedeschi, Rémi Emonet, Marc Sebban:
Fast and Provably Effective Multi-view Classification with Landmark-Based SVM. ECML/PKDD (2) 2018: 193-208 - 2017
- [c54]Jordan Fréry, Amaury Habrard, Marc Sebban, Olivier Caelen, Liyun He-Guelton:
Efficient Top Rank Optimization with Gradient Boosting for Supervised Anomaly Detection. ECML/PKDD (1) 2017: 20-35 - [c53]Ievgen Redko, Amaury Habrard, Marc Sebban:
Theoretical Analysis of Domain Adaptation with Optimal Transport. ECML/PKDD (2) 2017: 737-753 - [p1]Basura Fernando, Rahaf Aljundi, Rémi Emonet, Amaury Habrard, Marc Sebban, Tinne Tuytelaars:
Unsupervised Domain Adaptation Based on Subspace Alignment. Domain Adaptation in Computer Vision Applications 2017: 81-94 - [i7]Valentina Zantedeschi, Rémi Emonet, Marc Sebban:
L3-SVMs: Landmarks-based Linear Local Support Vectors Machines. CoRR abs/1703.00284 (2017) - 2016
- [j24]José Carlos Rangel, Miguel Cazorla, Ismael García-Varea, Jesus Martínez-Gómez, Élisa Fromont, Marc Sebban:
Scene classification based on semantic labeling. Adv. Robotics 30(11-12): 758-769 (2016) - [j23]Aurélien Bellet, José Francisco Bernabeu, Amaury Habrard, Marc Sebban:
Learning discriminative tree edit similarities for linear classification - Application to melody recognition. Neurocomputing 214: 155-161 (2016) - [j22]Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban:
A new boosting algorithm for provably accurate unsupervised domain adaptation. Knowl. Inf. Syst. 47(1): 45-73 (2016) - [c52]Valentina Zantedeschi, Rémi Emonet, Marc Sebban:
Metric Learning as Convex Combinations of Local Models with Generalization Guarantees. CVPR 2016: 1478-1486 - [c51]Valentina Zantedeschi, Rémi Emonet, Marc Sebban:
beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data. NIPS 2016: 4358-4366 - [i6]Valentina Zantedeschi, Rémi Emonet, Marc Sebban:
Lipschitz Continuity of Mahalanobis Distances and Bilinear Forms. CoRR abs/1604.01376 (2016) - [i5]Ievgen Redko, Amaury Habrard, Marc Sebban:
Theoretical Analysis of Domain Adaptation with Optimal Transport. CoRR abs/1610.04420 (2016) - [i4]Maria-Irina Nicolae, Éric Gaussier, Amaury Habrard, Marc Sebban:
Similarity Learning for Time Series Classification. CoRR abs/1610.04783 (2016) - 2015
- [b1]Aurélien Bellet, Amaury Habrard, Marc Sebban:
Metric Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers 2015, ISBN 978-3-031-00444-5 - [c50]Imtiaz Masud Ziko, Élisa Fromont, Damien Muselet, Marc Sebban:
Supervised spectral subspace clustering for visual dictionary creation in the context of image classification. ACPR 2015: 356-360 - [c49]Rahaf Aljundi, Rémi Emonet, Damien Muselet, Marc Sebban:
Landmarks-based kernelized subspace alignment for unsupervised domain adaptation. CVPR 2015: 56-63 - [c48]Maria-Irina Nicolae, Marc Sebban, Amaury Habrard, Éric Gaussier, Massih-Reza Amini:
Algorithmic Robustness for Semi-Supervised (ε, γ, τ) -Good Metric Learning. ICONIP (1) 2015: 253-263 - [c47]Maria-Irina Nicolae, Éric Gaussier, Amaury Habrard, Marc Sebban:
Joint Semi-supervised Similarity Learning for Linear Classification. ECML/PKDD (1) 2015: 594-609 - [c46]José Carlos Rangel, Miguel Cazorla, Ismael García-Varea, Jesus Martínez-Gómez, Élisa Fromont, Marc Sebban:
Computing Image Descriptors from Annotations Acquired from External Tools. ROBOT (2) 2015: 673-683 - [c45]Maria-Irina Nicolae, Marc Sebban, Amaury Habrard, Éric Gaussier, Massih-Reza Amini:
Algorithmic Robustness for Semi-Supervised (ε, γ, τ)-Good Metric Learning. ICLR (Workshop) 2015 - 2014
- [j21]Aurélien Bellet, Amaury Habrard, Emilie Morvant, Marc Sebban:
Learning a priori constrained weighted majority votes. Mach. Learn. 97(1-2): 129-154 (2014) - [c44]Michaël Perrot, Amaury Habrard, Damien Muselet, Marc Sebban:
Modeling Perceptual Color Differences by Local Metric Learning. ECCV (5) 2014: 96-111 - [i3]Basura Fernando, Amaury Habrard, Marc Sebban, Tinne Tuytelaars:
Subspace Alignment For Domain Adaptation. CoRR abs/1409.5241 (2014) - 2013
- [j20]Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban:
Iterative Self-Labeling Domain Adaptation for Linear Structured Image Classification. Int. J. Artif. Intell. Tools 22(5) (2013) - [c43]Basura Fernando, Amaury Habrard, Marc Sebban, Tinne Tuytelaars:
Unsupervised Visual Domain Adaptation Using Subspace Alignment. ICCV 2013: 2960-2967 - [c42]Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban:
Boosting for Unsupervised Domain Adaptation. ECML/PKDD (2) 2013: 433-448 - [i2]Marc Sebban, Richard Nock:
Combining Feature and Prototype Pruning by Uncertainty Minimization. CoRR abs/1301.3891 (2013) - [i1]Aurélien Bellet, Amaury Habrard, Marc Sebban:
A Survey on Metric Learning for Feature Vectors and Structured Data. CoRR abs/1306.6709 (2013) - 2012
- [j19]Aurélien Bellet, Amaury Habrard, Marc Sebban:
Good edit similarity learning by loss minimization. Mach. Learn. 89(1-2): 5-35 (2012) - [j18]Basura Fernando, Élisa Fromont, Damien Muselet, Marc Sebban:
Supervised learning of Gaussian mixture models for visual vocabulary generation. Pattern Recognit. 45(2): 897-907 (2012) - [c41]Basura Fernando, Élisa Fromont, Damien Muselet, Marc Sebban:
Discriminative feature fusion for image classification. CVPR 2012: 3434-3441 - [c40]Aurélien Bellet, Amaury Habrard, Marc Sebban:
Similarity Learning for Provably Accurate Sparse Linear Classification. ICML 2012 - [c39]Leonor Becerra-Bonache, Élisa Fromont, Amaury Habrard, Michaël Perrot, Marc Sebban:
Speeding Up Syntactic Learning Using Contextual Information. ICGI 2012: 49-53 - 2011
- [c38]Aurélien Bellet, Marc Sebban, Amaury Habrard:
An Experimental Study on Learning with Good Edit Similarity Functions. ICTAI 2011: 126-133 - [c37]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: 181-188 - [c36]Marc Bernard, Baptiste Jeudy, Jean-Philippe Peyrache, Marc Sebban, Franck Thollard:
Using the H-Divergence to Prune Probabilistic Automata. ICTAI 2011: 725-731 - [c35]Aurélien Bellet, Amaury Habrard, Marc Sebban:
Learning Good Edit Similarities with Generalization Guarantees. ECML/PKDD (1) 2011: 188-203 - 2010
- [j17]Aurélien Bellet, Marc Bernard, Thierry Murgue, Marc Sebban:
Learning state machine-based string edit kernels. Pattern Recognit. 43(6): 2330-2339 (2010) - [c34]Cécile Barat, Christophe Ducottet, Élisa Fromont, Anne-Claire Legrand, Marc Sebban:
Weighted Symbols-Based Edit Distance for String-Structured Image Classification. ECML/PKDD (1) 2010: 72-86
2000 – 2009
- 2009
- [j16]Jean-Christophe Janodet, Marc Sebban, Henri-Maxime Suchier:
Boosting Classifiers Built from Different Subsets of Features. Fundam. Informaticae 96(1-2): 89-109 (2009) - [j15]Stéphanie Jacquemont, François Jacquenet, Marc Sebban:
Mining probabilistic automata: a statistical view of sequential pattern mining. Mach. Learn. 75(1): 91-127 (2009) - [j14]Stéphanie Jacquemont, François Jacquenet, Marc Sebban:
A lower bound on the sample size needed to perform a significant frequent pattern mining task. Pattern Recognit. Lett. 30(11): 960-967 (2009) - [c33]Laurent Boyer, Olivier Gandrillon, Amaury Habrard, Mathilde Pellerin, Marc Sebban:
Learning Constrained Edit State Machines. ICTAI 2009: 734-741 - [c32]Stéphanie Jacquemont, François Jacquenet, Marc Sebban:
Discovering Patterns in Flows: A Privacy Preserving Approach with the ACSM Prototype. ECML/PKDD (2) 2009: 734-737 - 2008
- [j13]Marc Bernard, Laurent Boyer, Amaury Habrard, Marc Sebban:
Learning probabilistic models of tree edit distance. Pattern Recognit. 41(8): 2611-2629 (2008) - [c31]Laurent Boyer, Yann Esposito, Amaury Habrard, José Oncina, Marc Sebban:
SEDiL: Software for Edit Distance Learning. ECML/PKDD (2) 2008: 672-677 - [c30]Amaury Habrard, José Manuel Iñesta Quereda, David Rizo, Marc Sebban:
Melody Recognition with Learned Edit Distances. SSPR/SPR 2008: 86-96 - 2007
- [c29]Laurent Boyer, Amaury Habrard, Marc Sebban:
Learning Metrics Between Tree Structured Data: Application to Image Recognition. ECML 2007: 54-66 - [c28]Stéphanie Jacquemont, François Jacquenet, Marc Sebban:
Correct your text with Google. Web Intelligence 2007: 170-176 - 2006
- [j12]José Oncina, Marc Sebban:
Learning stochastic edit distance: Application in handwritten character recognition. Pattern Recognit. 39(9): 1575-1587 (2006) - [c27]Marc Bernard, Amaury Habrard, Marc Sebban:
Learning Stochastic Tree Edit Distance. ECML 2006: 42-53 - [c26]Marc Bernard, Jean-Christophe Janodet, Marc Sebban:
A Discriminative Model of Stochastic Edit Distance in the Form of a Conditional Transducer. ICGI 2006: 240-252 - [c25]Stéphanie Jacquemont, François Jacquenet, Marc Sebban:
Sequence Mining Without Sequences: A New Way for Privacy Preserving. ICTAI 2006: 347-354 - [c24]José Oncina, Marc Sebban:
Using Learned Conditional Distributions as Edit Distance. SSPR/SPR 2006: 403-411 - 2005
- [j11]Amaury Habrard, Marc Bernard, Marc Sebban:
Detecting Irrelevant Subtrees to Improve Probabilistic Learning from Tree-structured Data. Fundam. Informaticae 66(1-2): 103-130 (2005) - [j10]Jean-Christophe Janodet, Richard Nock, Marc Sebban, Henri-Maxime Suchier:
Adaptation du boosting à l'inférence grammaticale via l'utilisation d'un oracle de confiance. Rev. d'Intelligence Artif. 19(4-5): 713-740 (2005) - [c23]Stéphanie Jacquemont, François Jacquenet, Marc Sebban:
Constrained Sequence Mining based on Probabilistic Finite State Automata. CAP 2005: 15-30 - [c22]Amaury Habrard, Marc Bernard, Marc Sebban:
Correction of Uniformly Noisy Distributions to Improve Probabilistic Grammatical Inference Algorithms. FLAIRS 2005: 493-498 - 2004
- [c21]Jean-Christophe Janodet, Richard Nock, Marc Sebban, Henri-Maxime Suchier:
Boosting grammatical inference with confidence oracles. ICML 2004 - [c20]François Jacquenet, Marc Sebban, Georges Valétudie:
Mining Decision Rules from Deterministic Finite Automata. ICTAI 2004: 362-367 - 2003
- [j9]Richard Nock, Marc Sebban, Didier Bernard:
A Simple Locally Adaptive Nearest Neighbor Rule With Application To Pollution Forecasting. Int. J. Pattern Recognit. Artif. Intell. 17(8): 1369-1382 (2003) - [c19]Amaury Habrard, Marc Bernard, Marc Sebban:
Improvement of the State Merging Rule on Noisy Data in Probabilistic Grammatical Inference. ECML 2003: 169-180 - [c18]Marc Sebban, Henri-Maxime Suchier:
On Boosting Improvement: Error Reduction and Convergence Speed-Up. ECML 2003: 349-360 - [c17]Marc Sebban, Jean-Christophe Janodet:
On State Merging in Grammatical Inference: A Statistical Approach for Dealing with Noisy Data. ICML 2003: 688-695 - 2002
- [j8]Marc Sebban, Igor Mokrousov, Nalin Rastogi, Christophe Sola:
A data-mining approach to spacer oligonucleotide typing of Mycobacterium tuberculosis. Bioinform. 18(2): 235-243 (2002) - [j7]Marc Sebban, Richard Nock, Stéphane Lallich:
Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem. J. Mach. Learn. Res. 3: 863-885 (2002) - [j6]Marc Sebban, Richard Nock:
A hybrid filter/wrapper approach of feature selection using information theory. Pattern Recognit. 35(4): 835-846 (2002) - [c16]Franck Thollard, Marc Sebban, Philippe Ézéquel:
Boosting Density Function Estimators. ECML 2002: 431-443 - 2001
- [j5]Richard Nock, Marc Sebban:
Advances in Adaptive Prototype Weighting and Selection. Int. J. Artif. Intell. Tools 10(1-2): 137-155 (2001) - [j4]Richard Nock, Marc Sebban:
An improved bound on the finite-sample risk of the nearest neighbor rule. Pattern Recognit. Lett. 22(3/4): 407-412 (2001) - [j3]Richard Nock, Marc Sebban:
A Bayesian boosting theorem. Pattern Recognit. Lett. 22(3/4): 413-419 (2001) - [c15]Marc Sebban, Richard Nock:
Improvement of Nearest-Neighbor Classifiers via Support Vector Machines. FLAIRS 2001: 113-117 - [c14]Marc Sebban, Richard Nock, Stéphane Lallich:
Boosting Neighborhood-Based Classifiers. ICML 2001: 505-512 - 2000
- [j2]Marc Sebban, Richard Nock, Jean-Hugues Chauchat, Ricco Rakotomalala:
Impact of learning set quality and size on decision tree performances. Int. J. Comput. Syst. Signals 1(1): 85-105 (2000) - [c13]Marc Sebban, Richard Nock:
Identifying and Eliminating Irrelevant Instances Using Information Theory. AI 2000: 90-101 - [c12]Richard Nock, Marc Sebban:
Sharper Bounds for the Hardness of Prototype and Feature Selection. ALT 2000: 224-237 - [c11]Richard Nock, Marc Sebban, Pascal Jabby:
A Symmetric Nearest Neighbor Learning Rule. EWCBR 2000: 222-233 - [c10]Richard Nock, Marc Sebban:
A Boosting-Based Prototype Weighting and Selection Scheme. FLAIRS 2000: 71-75 - [c9]Marc Sebban, Richard Nock:
Instance Pruning as an Information Preserving Problem. ICML 2000: 855-862 - [c8]Marc Sebban, Richard Nock:
Contribution of Dataset Reduction Techniques to Tree-Simplification and Knowledge Discovery. PKDD 2000: 44-53 - [c7]Marc Sebban, Richard Nock:
Combining Feature and Example Pruning by Uncertainty Minimization. UAI 2000: 533-540
1990 – 1999
- 1999
- [c6]Marc Sebban, Gilles Richard:
From Theoretical Learnability to Statistical Measures of the Learnable. IDA 1999: 3-14 - [c5]Marc Sebban, Djamel A. Zighed, S. Di Palma:
Selection and Statistical Validation of Features and Prototypes. PKDD 1999: 184-192 - [c4]Marc Sebban, Richard Nock:
Contribution of Boosting in Wrapper Models. PKDD 1999: 214-222 - [c3]Richard Nock, Marc Sebban, Pascal Jappy:
Experiments on a Representation-Independent "Top-Down and Prune" Induction Scheme. PKDD 1999: 223-231 - 1998
- [c2]Marc Sebban, Anne M. Landraud:
Strings Clustering and Statistical Validation of Clusters. Canadian AI 1998: 298-309 - [c1]Marc Sebban:
Prototype Selection from Homogeneous Subsets by a Monte Carlo Sampling. FLAIRS 1998: 250-253 - 1996
- [j1]Sabine Rabaséda, Ricco Rakotomalala, Marc Sebban:
A Comparison of Some Contextual Discretization Methods. Inf. Sci. 92(1-4): 137-157 (1996)
Coauthor Index
[c75] [c74] [c73] [c72] [j33] [j32] [j31] [j29] [j28] [c66] [c64] [i11] [j27] [j26] [c62] [c61] [c60] [c59] [i10] [i9] [i8] [j25] [c58] [c57] [c56] [c54] [c53] [p1] [j23] [j22] [i5] [i4] [b1] [c48] [c47] [c45] [j21] [c44] [i3] [j20] [c43] [c42] [i1] [j19] [c40] [c39] [c38] [c37] [c35] [c33] [j13] [c31] [c30] [c29] [c27] [j11] [c22] [c19]
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