Marc Sebban

	2009
46		Stéphanie Jacquemont, François Jacquenet, Marc Sebban: Discovering Patterns in Flows: A Privacy Preserving Approach with the ACSM Prototype. ECML/PKDD (2) 2009: 734-737
45		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 30(11): 960-967 (2009)
	2008
44		Laurent Boyer, Yann Esposito, Amaury Habrard, José Oncina, Marc Sebban: SEDiL: Software for Edit Distance Learning. ECML/PKDD (2) 2008: 672-677
43		Amaury Habrard, José Manuel Iñesta Quereda, David Rizo, Marc Sebban: Melody Recognition with Learned Edit Distances. SSPR/SPR 2008: 86-96
42		Marc Bernard, Laurent Boyer, Amaury Habrard, Marc Sebban: Learning probabilistic models of tree edit distance. Pattern Recognition 41(8): 2611-2629 (2008)
	2007
41		Laurent Boyer, Amaury Habrard, Marc Sebban: Learning Metrics Between Tree Structured Data: Application to Image Recognition. ECML 2007: 54-66
40		Stéphanie Jacquemont, François Jacquenet, Marc Sebban: Correct your text with Google. Web Intelligence 2007: 170-176
	2006
39		Marc Bernard, Amaury Habrard, Marc Sebban: Learning Stochastic Tree Edit Distance. ECML 2006: 42-53
38		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
37		Stéphanie Jacquemont, François Jacquenet, Marc Sebban: Sequence Mining Without Sequences: A New Way for Privacy Preserving. ICTAI 2006: 347-354
36		José Oncina, Marc Sebban: Using Learned Conditional Distributions as Edit Distance. SSPR/SPR 2006: 403-411
35		José Oncina, Marc Sebban: Learning stochastic edit distance: Application in handwritten character recognition. Pattern Recognition 39(9): 1575-1587 (2006)
	2005
34		Stéphanie Jacquemont, François Jacquenet, Marc Sebban: Constrained Sequence Mining based on Probabilistic Finite State Automata. CAP 2005: 15-30
33		Amaury Habrard, Marc Bernard, Marc Sebban: Correction of Uniformly Noisy Distributions to Improve Probabilistic Grammatical Inference Algorithms. FLAIRS Conference 2005: 493-498
32		Amaury Habrard, Marc Bernard, Marc Sebban: Detecting Irrelevant Subtrees to Improve Probabilistic Learning from Tree-structured Data. Fundam. Inform. 66(1-2): 103-130 (2005)
31		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. Revue d'Intelligence Artificielle 19(4-5): 713-740 (2005)
	2004
30		Jean-Christophe Janodet, Richard Nock, Marc Sebban, Henri-Maxime Suchier: Boosting grammatical inference with confidence oracles. ICML 2004
29		François Jacquenet, Marc Sebban, Georges Valétudie: Mining Decision Rules from Deterministic Finite Automata. ICTAI 2004: 362-367
	2003
28		Amaury Habrard, Marc Bernard, Marc Sebban: Improvement of the State Merging Rule on Noisy Data in Probabilistic Grammatical Inference. ECML 2003: 169-180
27		Marc Sebban, Henri-Maxime Suchier: On Boosting Improvement: Error Reduction and Convergence Speed-Up. ECML 2003: 349-360
26		Marc Sebban, Jean-Christophe Janodet: On State Merging in Grammatical Inference: A Statistical Approach for Dealing with Noisy Data. ICML 2003: 688-695
25		Richard Nock, Marc Sebban, Didier Bernard: A Simple Locally Adaptive Nearest Neighbor Rule With Application To Pollution Forecasting. IJPRAI 17(8): 1369-1382 (2003)
	2002
24		Franck Thollard, Marc Sebban, Philippe Ézéquel: Boosting Density Function Estimators. ECML 2002: 431-443
23		Marc Sebban, I. Mokrousov, N. Rastogi, C. Sola: A data-mining approach to spacer oligonucleotide typing of Mycobacterium tuberculosis. Bioinformatics 18(2): 235-243 (2002)
22		Marc Sebban, Richard Nock, Stéphane Lallich: Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem. Journal of Machine Learning Research 3: 863-885 (2002)
21		Marc Sebban, Richard Nock: A hybrid filter/wrapper approach of feature selection using information theory. Pattern Recognition 35(4): 835-846 (2002)
	2001
20		Marc Sebban, Richard Nock: Improvement of Nearest-Neighbor Classifiers via Support Vector Machines. FLAIRS Conference 2001: 113-117
19		Marc Sebban, Richard Nock, Stéphane Lallich: Boosting Neighborhood-Based Classifiers. ICML 2001: 505-512
18		Richard Nock, Marc Sebban: Advances in Adaptive Prototype Weighting and Selection. International Journal on Artificial Intelligence Tools 10(1-2): 137-155 (2001)
17		Richard Nock, Marc Sebban: An improved bound on the finite-sample risk of the nearest neighbor rule. Pattern Recognition Letters 22(3/4): 407-412 (2001)
16		Richard Nock, Marc Sebban: A Bayesian boosting theorem. Pattern Recognition Letters 22(3/4): 413-419 (2001)
	2000
15		Richard Nock, Marc Sebban: Sharper Bounds for the Hardness of Prototype and Feature Selection. ALT 2000: 224-237
14		Marc Sebban, Richard Nock: Identifying and Eliminating Irrelevant Instances Using Information Theory. Canadian Conference on AI 2000: 90-101
13		Richard Nock, Marc Sebban, Pascal Jabby: A Symmetric Nearest Neighbor Learning Rule. EWCBR 2000: 222-233
12		Richard Nock, Marc Sebban: A Boosting-Based Prototype Weighting and Selection Scheme. FLAIRS Conference 2000: 71-75
11		Marc Sebban, Richard Nock: Instance Pruning as an Information Preserving Problem. ICML 2000: 855-862
10		Marc Sebban, Richard Nock: Contribution of Dataset Reduction Techniques to Tree-Simplification and Knowledge Discovery. PKDD 2000: 44-53
9		Marc Sebban, Richard Nock: Combining Feature and Example Pruning by Uncertainty Minimization. UAI 2000: 533-540
8		Marc Sebban, Richard Nock, Jean-Hugues Chauchat, Ricco Rakotomalala: Impact of learning set quality and size on decision tree performances. Int. J. Comput. Syst. Signal 1(1): 85-105 (2000)
	1999
7		Marc Sebban, Gilles Richard: From Theoretical Learnability to Statistical Measures of the Learnable. IDA 1999: 3-14
6		Marc Sebban, Djamel A. Zighed, S. Di Palma: Selection and Statistical Validation of Features and Prototypes. PKDD 1999: 184-192
5		Marc Sebban, Richard Nock: Contribution of Boosting in Wrapper Models. PKDD 1999: 214-222
4		Richard Nock, Marc Sebban, Pascal Jappy: Experiments on a Representation-Independent "Top-Down and Prune" Induction Scheme. PKDD 1999: 223-231
	1998
3		Marc Sebban, Anne M. Landraud: Strings Clustering and Statistical Validation of Clusters. Canadian Conference on AI 1998: 298-309
2		Marc Sebban: Prototype Selection from Homogeneous Subsets by a Monte Carlo Sampling. FLAIRS Conference 1998: 250-253
	1996
1		Sabine Rabaséda, Ricco Rakotomalala, Marc Sebban: A Comparison of Some Contextual Discretization Methods. Inf. Sci. 92(1-4): 137-157 (1996)

Coauthor Index

1	Didier Bernard	[25]
2	Marc Bernard	[28] [32] [33] [38] [39] [42]
3	Laurent Boyer	[41] [42] [44]
4	Jean-Hugues Chauchat	[8]
5	Yann Esposito	[44]
6	Philippe Ézéquel	[24]
7	Amaury Habrard	[28] [32] [33] [39] [41] [42] [43] [44]
8	Pascal Jabby	[13]
9	Stéphanie Jacquemont	[34] [37] [40] [45] [46]
10	François Jacquenet	[29] [34] [37] [40] [45] [46]
11	Jean-Christophe Janodet	[26] [30] [31] [38]
12	Pascal Jappy	[4]
13	Stéphane Lallich	[19] [22]
14	Anne M. Landraud	[3]
15	I. Mokrousov	[23]
16	Richard Nock	[4] [5] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [25] [30] [31]
17	José Oncina	[35] [36] [44]
18	S. Di Palma	[6]
19	José Manuel Iñesta Quereda	[43]
20	Sabine Loudcher Rabaséda (Sabine Rabaséda)	[1]
21	Ricco Rakotomalala	[1] [8]
22	N. Rastogi	[23]
23	Gilles Richard	[7]
24	David Rizo	[43]
25	C. Sola	[23]
26	Henri-Maxime Suchier	[27] [30] [31]
27	Franck Thollard	[24]
28	Georges Valétudie	[29]
29	Djamel A. Zighed (Djamel Abdelkader Zighed)	[6]

Colors in the list of coauthors