LIST OF PUBLICATIONS OF MARC SEBBAN from January 2005 to December 2021
2021
- [Barbe et al. 2021] A. Barbe, P. Gonçalves, M. Sebban,
P. Borgnat, R. Gribonval, T. Vayer,
Optimization of the Diffusion Time in Graph Diffused-Wasserstein Distances: Application to Domain Adaptation, ICTAI 2021 - 33rd IEEE International Conference on Tools with Artificial Intelligence, pp. 1-8, Virtual conference, France (2021)
-
CORE Ranking : B
- [Azorin-Lopez et al. 2021] J. Azorin-Lopez, M. Sebban,
A. Fuster-Guillo, M. Saval-Calvo, A. Habrard,
Iterative multilinear optimization for planar model fitting under geometric constraints, PeerJ Computer Science, (2021)
-
IF : 2.411 (Q2)
- [Viola et al. 2021] R. Viola,
R. Emonet,
A. Habrard,
G. Metzler,
S. Riou, M. Sebban,
A Nearest Neighbor Algorithm for Imbalanced Classification, International Journal on Artificial Intelligence Tools, vol. 30(), (2021)
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IF : 1.059 (Q4)
- [Kerdoncuff et al. 2021b] T. Kerdoncuff,
R. Emonet,
M. Sebban,
Metric Learning in Optimal Transport for Domain Adaptation, International Joint Conference on Artificial Intelligence, Kyoto, Japan (2021)
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CORE Ranking : A*
- [Kerdoncuff et al. 2021a] T. Kerdoncuff,
R. Emonet,
M. Sebban,
Sampled Gromov Wasserstein, Machine Learning, (2021)
-
IF : 5.414 (Q2)
2020
- [Barbe et al. 2020b] A. Barbe, M. Sebban,
P. Gonçalves, P. Borgnat, R. Gribonval,
Transport Optimal entre Graphes exploitant la Diffusion de la Chaleur, CAP 2020 - Conférence sur l'Apprentissage Automatique, Vannes, France (2020)
- [Barbe et al. 2020a] A. Barbe, M. Sebban,
P. Gonçalves, P. Borgnat, R. Gribonval,
Graph Diffusion Wasserstein Distances, ECML PKDD 2020 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pp. 1-16, Ghent, Belgium (2020)
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CORE Ranking : A
- [Gautheron et al. 2020b] L. Gautheron,
P. Germain, A. Habrard,
G. Metzler,
E. Morvant,
M. Sebban,
V. Zantedeschi,
Landmark-based Ensemble Learning with Random Fourier Features and Gradient Boosting, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Ghent, Belgium (2020)
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CORE Ranking : A
- [Dhouib et al. 2020] S. Dhouib, I. Redko,
T. Kerdoncuff,
R. Emonet,
M. Sebban,
A Swiss Army Knife for Minimax Optimal Transport, Thirty-seventh International Conference on Machine Learning, Vienne, Austria (2020)
- [Viola et al. 2020a] R. Viola,
R. Emonet,
A. Habrard,
G. Metzler,
M. Sebban,
Learning from Few Positives: a Provably Accurate Metric Learning Algorithm to deal with Imbalanced Data, IJCAI 2020, the 29th International Joint Conference on Artificial Intelligence, Yokohama, Japan (2020)
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CORE Ranking : A*
- [Viola et al. 2020b] R. Viola,
R. Emonet,
A. Habrard,
G. Metzler,
M. Sebban,
MLFP: Un algorithme d'apprentissage de métrique pour la classification de données déséquilibrées, Conférence sur l'Apprentissage automatique (CAp 2020), Vannes, France (2020)
- [Gautheron et al. 2020a] L. Gautheron,
A. Habrard,
E. Morvant,
M. Sebban,
Metric Learning from Imbalanced Data with Generalization Guarantees, Pattern Recognition Letters, vol. 133(), pp. 298-304 (2020)
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IF : 3.756 (Q2)
2019
- [Gautheron et al. 2019a] L. Gautheron,
E. Morvant,
A. Habrard,
M. Sebban,
Metric Learning from Imbalanced Data, International Conference on Tools with Artificial Intelligence (ICTAI), Portland, Oregon, United States (2019)
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CORE Ranking : B
- [Viola et al. 2019a] R. Viola,
R. Emonet,
A. Habrard,
G. Metzler,
S. Riou, M. Sebban,
An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data, International Conference on Tools with Artificial Intelligence (ICTAI), Portland, Oregon, United States (2019)
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CORE Ranking : B
- [Barbe et al. 2019] D. Barbe, P. Borgnat, P. Gonçalves, M. Sebban,
Transport Optimal sous Contrainte de Régularité pour l'Adaptation de Domaines entre Graphes avec Attributs, GRETSI 2019 - XXVIIème Colloque francophonede traitement du signal et des images, pp. 1-4, Lille, France (2019)
- [Le et al. 2019b] T. N. Le,
A. Habrard,
M. Sebban,
Differentially Private Optimal Transport: Application to Domain Adaptation, IJCAI 2019, Macao, China (2019)
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CORE Ranking : A*
- [Gautheron et al. 2019b] L. Gautheron,
P. Germain, A. Habrard,
G. Letarte, E. Morvant,
M. Sebban,
V. Zantedeschi,
Revisite des "random Fourier features" basée sur l'apprentissage PAC-Bayésien via des points d'intérêts, CAp 2019 - Conférence sur l'Apprentissage automatique, Toulouse, France (2019)
- [Viola et al. 2019b] R. Viola,
R. Emonet,
A. Habrard,
G. Metzler,
S. Riou, M. Sebban,
Une version corrigée de l’algorithme des plus proches voisins pour l’optimisation de la F-mesure dans un contexte déséquilibré, Conférence sur l'Apprentissage automatique (CAp 2019), Toulouse, France (2019)
- [Redko et al. 2019] I. Redko,
A. Habrard,
M. Sebban,
On the analysis of adaptability in multi-source domain adaptation, Machine Learning, (2019)
-
IF : 2.672 (Q2)
- [Bascol et al. 2019] K. Bascol,
R. Emonet,
E. Fromont,
A. Habrard,
G. Metzler,
M. Sebban,
From Cost-Sensitive Classification to Tight F-measure Bounds, AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics, pp. 1245-1253, Naha, Okinawa, Japan (2019)
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CORE Ranking : A
- [Le et al. 2019a] T. N. Le,
A. Habrard,
M. Sebban,
Deep Multi-Wasserstein Unsupervised Domain Adaptation, Pattern Recognition Letters, (2019)
-
IF : 3.255 (Q2)
2018
- [Frery et al. 2018] J. Frery,
A. Habrard,
M. Sebban,
O. Caelen, L. He-Guelton,
Online Non-Linear Gradient Boosting in Multi-Latent Spaces, 17th International Symposium on Intelligent Data Analysis (IDA'18), s-Hertogenbosch, Netherlands (2018)
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CORE Ranking : A
- [Metzler et al. 2018b] G. Metzler,
X. Badiche, B. Belkasmi, E. Fromont,
A. Habrard,
M. Sebban,
Tree-based Cost-Sensitive Methods for Fraud Detection in Imbalanced Data, IDA 2018 - 17th International Symposium on Intelligent Data Analysis, pp. 213-224, ‘s-Hertogenbosch, Netherlands (2018)
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CORE Ranking : A
- [Metzler et al. 2018a] G. Metzler,
X. Badiche, B. Belkasmi, E. Fromont,
A. Habrard,
M. Sebban,
Learning maximum excluding ellipsoids from imbalanced data with theoretical guarantees, Pattern Recognition Letters, vol. 112(), pp. 310-316 (2018)
-
IF : 2.81 (Q2)
- [Zantedeschi et al. 2018] V. Zantedeschi,
R. Emonet,
M. Sebban,
Fast and Provably Effective Multi-view Classification with Landmark-based SVM, ECML PKDD 2018, Dublin, Ireland (2018)
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CORE Ranking : A
- [Bascol et al. 2018] K. Bascol,
R. Emonet,
E. Fromont,
A. Habrard,
G. Metzler,
M. Sebban,
Un algorithme de pondération de la F-Mesure par pondération des erreurs de classfication., Conférence pour l'Apprentissage Automatique, Saint-Etienne du Rouvray, France (2018)
- [Gautheron et al. 2018] L. Gautheron,
A. Habrard,
E. Morvant,
M. Sebban,
Apprentissage de métrique pour la classification supervisée de données déséquilibrées, Conférence sur l'Apprentissage Automatique, Rouen, France (2018)
2017
- [Frery et al. 2017] J. Frery,
A. Habrard,
M. Sebban,
O. Caelen, L. He-Guelton,
Efficient top rank optimization with gradient boosting for supervised anomaly detection, ECML-PKDD 2017, Skopje, Macedonia (2017)
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CORE Ranking : A
- [Redko et al. 2017] I. Redko,
A. Habrard,
M. Sebban,
Theoretical Analysis of Domain Adaptation with Optimal Transport, ECML PKDD 2017, Skopje, Macedonia (2017)
-
CORE Ranking : A
- [Metzler et al. 2017] G. Metzler,
X. Badiche, B. Belkasmi, S. Canu, E. Fromont,
A. Habrard,
M. Sebban,
Apprentissage de sphères maximales d’exclusion avec garanties théoriques, Conférence sur l'Apprentissage Automatique, Grenoble, France (2017)
2016
- [Zantedeschi et al. 2016a] V. Zantedeschi,
R. Emonet,
M. Sebban,
beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data, NIPS 2016, Barcelona, Spain (2016)
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CORE Ranking : A*
- [Zantedeschi et al. 2016c] V. Zantedeschi,
R. Emonet,
M. Sebban,
Apprentissage de Combinaisons Convexes de Métriques Locales avec Garanties de Généralisation, CAp2016, Marseille, France (2016)
- [Zantedeschi et al. 2016b] V. Zantedeschi,
R. Emonet,
M. Sebban,
Metric Learning as Convex Combinations of Local Models with Generalization Guarantees, CVPR2016, Las Vegas, United States (2016)
-
CORE Ranking : A
- [José Carlos et al. 2016] J. José Carlos, C. Miguel, G. V. Ismael, M. Jesus, E. Fromont,
M. Sebban,
Scene classification based on semantic labeling, Advanced Robotics, vol. 30(11), pp. 758-769 (2016)
-
IF : 0.92 (Q4)
2015
- [Irina et al. 2015a] N. Irina,
M. Sebban,
A. Habrard,
E. Gaussier, M. R. Amini,
Algorithmic Robustness for Semi-Supervised (ε, γ, τ )-Good Metric Learning, International Conference on Neural Information Processing ICONIP, pp. 10, Istanbul, Turkey (2015)
-
CORE Ranking : A
- [Rangel et al. 2015] J. Rangel, M. Cazorla, I. García-Varea, J. Martínez-Gómez, E. Fromont,
M. Sebban,
Computing Image Descriptors from Annotations Acquired from External Tools, ROBOT 2015: Second Iberian Robotics Conference, Lisbon, Portugal (2015)
- [Ziko et al. 2015] I. Ziko,
E. Fromont,
D. Muselet,
M. Sebban,
Supervised Spectral Subspace Clustering for Visual Dictionary Creation in the Context of Image Classification, ACPR 2015: 3rd IAPR Asian Conference on Pattern Recognition, Kuala Lumpur, Malaysia (2015)
- [Cognasse et al. 2015] F. Cognasse, A. Chaker, K. Anh Nguyen, H. Hamzeh-Cognasse, F. Jocelyne, A. Charles-Antoine, E. Marie-Ange, M. Sebban,
E. Fromont,
B. Pozzetto, S. Laradi,
O. Garraud,
Platelet components associated with adverse reactions: predictive value of mitochondrial DNA relative to biological response modifiers, Transfusion, (2015)
-
IF : 3.042 (Q2)
- [Irina et al. 2015b] N. Irina,
G. Eric, A. Habrard,
M. Sebban,
Joint Semi-supervised Similarity Learning for Linear Classification, ECML-PKDD 2015, Porto, Portugal (2015)
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CORE Ranking : A
- [Aljundi et al. 2015] R. Aljundi,
R. Emonet,
D. Muselet,
M. Sebban,
Landmarks-based Kernelized Subspace Alignment for Unsupervised Domain Adaptation, Computer Vision and Pattern Recognition (CVPR'2015), Boston, United States (2015)
-
CORE Ranking : A
- [Habrard et al. 2015] A. Habrard,
J. P. Peyrache,
M. Sebban,
A New Boosting Algorithm for Provably Accurate Unsupervised Domain Adaptation, Knowledge and Information Systems (KAIS), pp. 1 (2015)
-
IF : 1.702 (Q2)
2014
- [Perrot et al. 2014a] M. Perrot,
A. Habrard,
D. Muselet,
M. Sebban,
Modeling Perceptual Color Differences by Local Metric Learning, European Conference on Computer Vision, Zurich, Switzerland (2014)
-
CORE Ranking : A
- [Perrot et al. 2014b] M. Perrot,
A. Habrard,
D. Muselet,
M. Sebban,
Modélisation de Distances Couleur Uniformes par Apprentissage de Métriques Locales, CAp'2014 : Conférence d'Apprentissage Automatique, Saint-Étienne, France (2014)
- [Anh Nguyen et al. 2014] K. Anh Nguyen, H. Hamzeh-Cognasse, M. Sebban,
E. Fromont,
P. Chavarin, L. Absi, B. Pozzetto, F. Cognasse, O. Garraud,
A Computerized Prediction Model of Hazardous Inflammatory Platelet Transfusion Outcomes, PLoS ONE, pp. 9(5): e97082 (2014)
-
IF : 3.234 (Q1)
- [Bellet et al. 2014] A. Bellet,
A. Habrard,
E. Morvant,
M. Sebban,
Learning A Priori Constrained Weighted Majority Votes, Machine Learning, vol. 97(1), pp. 129-154 (2014)
-
IF : 1.889 (Q2)
2013
- [Fernando et al. 2013] B. Fernando,
A. Habrard,
M. Sebban,
T. Tuytelaars,
Unsupervised Visual Domain Adaptation Using Subspace Alignment, ICCV 2013, pp. 2960-2967, Sydney, Australia (2013)
-
CORE Ranking : A*
- [Habrard et al. 2013a] A. Habrard,
J. P. Peyrache,
M. Sebban,
Iterative Self-labeling Domain Adaptation for Linear Structured Image Classification, International Journal on Artificial Intelligence Tools, (2013)
-
IF : 0.321 (Q4)
- [Habrard et al. 2013b] A. Habrard,
J. P. Peyrache,
M. Sebban,
Boosting for Unsupervised Domain Adaptation, ECML PKDD 2013, pp. 433-448, Prague, Czech Republic (2013)
- [Bellet et al. 2013] A. Bellet,
A. Habrard,
E. Morvant,
M. Sebban,
Vote de majorité a priori contraint pour la classification binaire : spécification au cas des plus proches voisins, Conférence sur l'Apprentissage Automatique (CAp), Lille, France (2013)
2012
- [Becerra-Bonache et al. 2012] L. Becerra-Bonache,
E. Fromont,
A. Habrard,
M. Perrot,
M. Sebban,
Speeding Up Syntactic Learning Using Contextual Information, International Conference on Grammatical Inference, pp. 49-53, United States (2012)
- [Bellet et al. 2012b] A. Bellet,
A. Habrard,
M. Sebban,
Similarity Learning for Provably Accurate Sparse Linear Classification, International Conference on Machine Learning, Great Britain (2012)
-
CORE Ranking : A*
- [Bernard et al. 2012] M. Bernard,
E. Fromont,
A. Habrard,
M. Sebban,
Handwritten Digit Recognition using Edit Distance-Based KNN, Teaching Machine Learning Workshop, Edinburgh, Scotland, Great Britain (2012)
- [Fernando et al. 2012b] B. Fernando,
E. Fromont,
D. Muselet,
M. Sebban,
Discriminative Feature Fusion for Image Classification, Computer Vision and Pattern Recognition, pp. 3434-3441, Rhode Island, United States (2012)
-
CORE Ranking : A
- [Bellet et al. 2012c] A. Bellet,
A. Habrard,
M. Sebban,
Apprentissage de bonnes similarités pour la classification linéaire parcimonieuse, Conférence Francophone sur l'Apprentissage Automatique - CAp 2012, pp. 16 p., Nancy, France (2012)
- [Habrard et al. 2012] A. Habrard,
J. P. Peyrache,
M. Sebban,
Un Cadre Formel de Boosting pour l'Adaptation de Domaine, Conférence Francophone sur l'Apprentissage Automatique - CAp 2012, pp. 16 p., Nancy, France (2012)
- [Bellet et al. 2012a] A. Bellet,
A. Habrard,
M. Sebban,
Good edit similarity learning by loss minimization, Machine Learning, pp. 5-35 (2012)
-
IF : 1.454 (Q2)
- [Fernando et al. 2012a] B. Fernando,
E. Fromont,
D. Muselet,
M. Sebban,
Supervised Learning of Gaussian Mixture Models forVisual Vocabulary Generation, Pattern Recognition, vol. 45(2), pp. 897-907 (2012)
-
IF : 2.632 (Q1)
2011
- [Bellet et al. 2011a] A. Bellet,
A. Habrard,
M. Sebban,
An Experimental Study on Learning with Good Edit Similarity Functions, ICTAI 2011, United States (2011)
-
CORE Ranking : B
- [Bernard et al. 2011] M. Bernard,
B. Jeudy,
J. P. Peyrache,
M. Sebban,
F. Thollard,
Using the H-divergence to Prune Probabilistic Automata, ICTAI 2011, Boca Raton, United States (2011)
-
CORE Ranking : B
- [Habrard et al. 2011] A. Habrard,
J. P. Peyrache,
M. Sebban,
Domain Adaptation with Good Edit Similarities: a Sparse Way to deal with Scaling and Rotation Problems in Image Classification, ICTAI 2011, United States (2011)
-
CORE Ranking : B
- [Bellet et al. 2011b] A. Bellet,
A. Habrard,
M. Sebban,
Learning Good Edit Similarities with Generalization Guarantees, European Conference on Machine Learning, pp. 188-203, Athens, Greece (2011)
2010
- [Fernando et al. 2010] B. Fernando,
E. Fromont,
D. Muselet,
M. Sebban,
Accurate Visual Word Construction using a Supervised Approach, 25th International Conference of Image and Vision Computing New Zealand, New Zealand (2010)
-
CORE Ranking : A
- [Barat et al. 2010] C. Barat,
C. Ducottet,
E. Fromont,
A. C. Legrand,
M. 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., pp. to appear, Barcelona, Spain (2010)
- [Bellet et al. 2010] A. Bellet,
M. Bernard,
T. Murgue,
M. Sebban,
Learning State Machine-based String Edit Kernels, Pattern Recognition, vol. 43(2010), pp. 2330-2339 (2010)
-
IF : 2.682 (Q1)
2009
- [Boyer et al. 2009] L. Boyer,
O. Gandrillon, A. Habrard,
M. Pellerin, M. Sebban,
Learning Constrained Edit State Machines, 21st IEEE International Conference on Tools with Artificial Intelligence, pp. 734-741, United States (2009)
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CORE Ranking : B
- [Jacquemont et al. 2009c] S. Jacquemont,
F. Jacquenet,
M. Sebban,
Discovering Patterns in Flows: a Privacy Preserving Approach with the ACSM Prototype, ECML PKDD, pp. 734--737, Bled, Slovenia (2009)
- [Janodet et al. 2009] J. C. Janodet,
M. Sebban,
H. M. Suchier,
Boosting Classifiers built from Different Subsets of Features, Fundamenta Informaticae, vol. 94(2009), pp. 1-21 (2009)
-
IF : 0.615 (Q3)
- [Bellet et al. 2009] A. Bellet,
M. Bernard,
T. Murgue,
M. Sebban,
Apprentissage de noyaux d'édition de séquences, Conférence d'Apprentissage : CAP 2009, pp. à venir, Hammamet, Tunisia (2009)
- [Jacquemont et al. 2009b] S. Jacquemont,
F. Jacquenet,
M. Sebban,
A Lower Bound on the Sample Size needed to perform a Significant Frequent Pattern Mining Task, Pattern Recognition Letters, vol. 30(2009), pp. 960-967 (2009)
-
IF : 1.303 (Q3)
- [Jacquemont et al. 2009a] S. Jacquemont,
F. Jacquenet,
M. Sebban,
Mining Probabilistic Automata: A Statistical View of Sequential Pattern Mining, Machine Learning, vol. 75(1), pp. 91-127 (2009)
-
IF : 1.663 (Q2)
2008
- [Habrard et al. 2008] A. Habrard,
J. M. Inesta, D. Rizo, M. Sebban,
Melody Recognition with Learned Edit Distances, Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshops, SSPR 2008 and SPR 2008, pp. 86-96, Orlando, United States (2008)
-
CORE Ranking : A
- [Boyer et al. 2008a] L. Boyer,
Y. Esposito, A. Habrard,
J. Oncina, M. Sebban,
SEDiL: Software for Edit Distance Learning, European Conference on Machine Learning (ECML 2008), pp. 672-677, Belgium (2008)
- [Boyer et al. 2008b] L. Boyer,
A. Habrard,
F. Muhlenbach,
M. Sebban,
Learning String Edit Similarities using Constrained Finite State Machines, CAp'08, pp. 37-52, Porquerolles, France (2008)
- [Bernard et al. 2008] M. Bernard,
L. Boyer,
A. Habrard,
M. Sebban,
Learning Probabilistic Models of Tree Edit Distance, Pattern Recognition, vol. 41(8), pp. 2611-2629 (2008)
-
IF : 3.279 (Q1)
2007
- [Jacquemont et al. 2007] S. Jacquemont,
F. Jacquenet,
M. Sebban,
Correct your Text with Google, IEEE International Conference on Web Intelligence, pp. xx-xx, Fremont, United States (2007)
- [Boyer et al. 2007] L. Boyer,
A. Habrard,
M. Sebban,
Learning Metrics between Tree Structured Data: Application to Image Recognition, 18th European Conference on Machine Learning (ECML), pp. 54-66, Warsaw, Poland (2007)
2006
- [Bernard et al. 2006a] M. Bernard,
J. C. Janodet,
M. Sebban,
A Discriminative Model of Stochastic Edit Distance in the form of a Conditional Transducer, 8th International Colloquium on Grammatical Inference, pp. 240-252, Tokyo, Japan (2006)
- [Bernard et al. 2006b] M. Bernard,
A. Habrard,
M. Sebban,
Learning Stochastic Tree Edit Distance, 17th European Conference on Machine Learning, pp. 42-53, Berlin, Germany (2006)
- [Oncina and Sebban 2006b] J. Oncina, M. Sebban,
Using Learned Conditional Distributions as Edit Distance, Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshops, SSPR 2006 and SPR 2006, pp. pp 403-411, ISBN 3-540-37236-9, Hong Kong, China (2006)
- [Bernard et al. 2006c] M. Bernard,
J. C. Janodet,
M. Sebban,
Learning Conditional Transducers for Estimating the Distribution of String Edit Costs, Grammatical Inference: workshop on open problems and new directions, Saint-Etienne, France (2006)
- [Janodet et al. 2006] J. C. Janodet,
H. M. Suchier,
M. Sebban,
C. Largeron,
Boosting d'un pool d'apprenants faibles, CAp 2006, pp. 283-298, Trégastel, France (2006)
- [Jacquemont et al. 2006] S. Jacquemont,
F. Jacquenet,
M. Sebban,
Sequence Mining Without Sequences: a New Way for Privacy Preserving, Conference title from HAL not found, pp. 347-354, (2006)
- [Oncina and Sebban 2006a] J. Oncina, M. Sebban,
Learning Stochastic Edit Distance: application in handwritten character recognition, Pattern Recognition, vol. 39(), pp. 1575-1587 (2006)
-
IF : 1.822 (Q1)
2005
- [Habrard et al. 2005a] A. Habrard,
M. Bernard,
M. Sebban,
Detecting Irrelevant subtrees to improve probabilistic learning from tree-structured data, Fundamenta Informaticae, vol. 66(1), pp. 103-130 (2005)
-
IF : 0.65 (Q2)
- [Jacquemont et al. 2005a] S. Jacquemont,
F. Jacquenet,
M. Sebban,
Constrained Sequence Mining based on Probabilistic Finite State Automata, Workshop on Mining Graphs, Trees and Structured Data at ECML/PKDD, Portugal (2005)
- [Janodet et al. 2005] J. C. Janodet,
M. Sebban,
H. M. Suchier,
R. 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, vol. 19(4), pp. 713-740 (2005)
- [Habrard et al. 2005b] A. Habrard,
M. Bernard,
M. Sebban,
Correction of Uniformly Noisy Distributions to Improve Probabilistic Grammatical Inference Algorithms, 18th International Florida Artificial Intelligence Research Society conference, pp. 493-498, United States (2005)
- [Jacquemont et al. 2005b] S. Jacquemont,
F. Jacquenet,
M. Sebban,
Constrained Sequence Mining based on Probabilistic Finite State Automata, Conférence d'Apprentissage Automatique, pp. 15-30, France (2005)