| 2008 |
|---|
| 88 | EE | Xiangliang Zhang,
Cyril Furtlehner,
Michèle Sebag:
Data Streaming with Affinity Propagation.
ECML/PKDD (2) 2008: 628-643 |
| 87 | EE | Luís Da Costa,
Álvaro Fialho,
Marc Schoenauer,
Michèle Sebag:
Adaptive operator selection with dynamic multi-armed bandits.
GECCO 2008: 913-920 |
| 86 | EE | Álvaro Fialho,
Luís Da Costa,
Marc Schoenauer,
Michèle Sebag:
Extreme Value Based Adaptive Operator Selection.
PPSN 2008: 175-184 |
| 85 | | Xiangliang Zhang,
Cyril Furtlehner,
Michèle Sebag:
Distributed and Incremental Clustering Based on Weighted Affinity Propagation.
STAIRS 2008: 199-210 |
| 84 | EE | Alexandre Termier,
Marie-Christine Rousset,
Michèle Sebag,
Kouzou Ohara,
Takashi Washio,
Hiroshi Motoda:
DryadeParent, An Efficient and Robust Closed Attribute Tree Mining Algorithm.
IEEE Trans. Knowl. Data Eng. 20(3): 300-320 (2008) |
| 83 | EE | Antoine Cornuéjols,
Michèle Sebag:
A note on phase transitions and computational pitfalls of learning from sequences.
J. Intell. Inf. Syst. 31(2): 177-189 (2008) |
| 2007 |
|---|
| 82 | EE | Christian Gagné,
Michèle Sebag,
Marc Schoenauer,
Marco Tomassini:
Ensemble learning for free with evolutionary algorithms?
GECCO 2007: 1782-1789 |
| 81 | EE | Xiangliang Zhang,
Michèle Sebag,
Cécile Germain:
Toward Behavioral Modeling of a Grid System: Mining the Logging and Bookkeeping Files.
ICDM Workshops 2007: 581-588 |
| 80 | EE | Nicolas Baskiotis,
Michèle Sebag,
Marie-Claude Gaudel,
Sandrine-Dominique Gouraud:
A Machine Learning Approach for Statistical Software Testing.
IJCAI 2007: 2274-2279 |
| 79 | EE | Romaric Gaudel,
Michèle Sebag,
Antoine Cornuéjols:
A Phase Transition-Based Perspective on Multiple Instance Kernels.
ILP 2007: 112-121 |
| 78 | EE | Nicolas Baskiotis,
Michèle Sebag:
Structural Statistical Software Testing with Active Learning in a Graph.
ILP 2007: 49-62 |
| 77 | EE | Nicolas Baskiotis,
Michèle Sebag:
Structural Sampling for Statistical Software Testing.
Probabilistic, Logical and Relational Learning - A Further Synthesis 2007 |
| 76 | EE | Christian Gagné,
Michèle Sebag,
Marc Schoenauer,
Marco Tomassini:
Ensemble Learning for Free with Evolutionary Algorithms ?
CoRR abs/0704.3905: (2007) |
| 75 | EE | Nicolas Godzik,
Marc Schoenauer,
Michèle Sebag:
Evolving Symbolic Controllers
CoRR abs/0705.1244: (2007) |
| 2006 |
|---|
| 74 | EE | Christian Gagné,
Marc Schoenauer,
Michèle Sebag,
Marco Tomassini:
Genetic Programming for Kernel-Based Learning with Co-evolving Subsets Selection.
PPSN 2006: 1008-1017 |
| 73 | EE | Vojtech Krmicek,
Michèle Sebag:
Functional Brain Imaging with Multi-objective Multi-modal Evolutionary Optimization.
PPSN 2006: 382-391 |
| 72 | EE | Marc Schoenauer,
Michèle Sebag:
Using Domain Knowledge in Evolutionary System Identification
CoRR abs/cs/0602021: (2006) |
| 71 | EE | Alain Ratle,
Michèle Sebag:
Avoiding the Bloat with Stochastic Grammar-based Genetic Programming
CoRR abs/cs/0602022: (2006) |
| 70 | EE | Christian Gagné,
Marc Schoenauer,
Michèle Sebag,
Marco Tomassini:
Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection
CoRR abs/cs/0611135: (2006) |
| 69 | EE | Vojtech Krmicek,
Michèle Sebag:
Functional Brain Imaging with Multi-Objective Multi-Modal Evolutionary Optimization
CoRR abs/cs/0611138: (2006) |
| 2005 |
|---|
| 68 | | Sylvain Gelly,
Nicolas Bredeche,
Michèle Sebag:
HMM hiérarchiques et factorisés: mécanisme d'inférence et apprentissage à partir de peu de données.
CAP 2005: 143-144 |
| 67 | | Nicolas Baskiotis,
Michèle Sebag,
Olivier Teytaud:
Systèmes inductifs-déductifs: une approche statistique.
CAP 2005: 145-146 |
| 66 | | Nicolas Tarrisson,
Michèle Sebag,
Olivier Teytaud,
Julien Lefevre,
Sylvain Baillet:
Multi-objective Multi-modal Optimization for Mining Spatio-temporal Patterns.
CAP 2005: 217-230 |
| 65 | | Nicolas Pernot,
Antoine Cornuéjols,
Michèle Sebag:
Phase transitions in grammatical inference.
CAP 2005: 49-60 |
| 64 | EE | Elena Marchiori,
Michèle Sebag:
Bayesian Learning with Local Support Vector Machines for Cancer Classification with Gene Expression Data.
EvoWorkshops 2005: 74-83 |
| 63 | EE | Alexandre Termier,
Marie-Christine Rousset,
Michèle Sebag,
Kouzou Ohara,
Takashi Washio,
Hiroshi Motoda:
Efficient Mining of High Branching Factor Attribute Trees.
ICDM 2005: 785-788 |
| 62 | EE | Nicolas Pernot,
Antoine Cornuéjols,
Michèle Sebag:
Phase Transitions within Grammatical Inference.
IJCAI 2005: 811-816 |
| 61 | EE | Michèle Sebag,
Nicolas Tarrisson,
Olivier Teytaud,
Julien Lefevre,
Sylvain Baillet:
A Multi-Objective Multi-Modal Optimization Approach for Mining Stable Spatio-Temporal Patterns.
IJCAI 2005: 859-864 |
| 60 | EE | Sylvain Gelly,
Nicolas Bredeche,
Michèle Sebag:
From Factorial and Hierarchical HMM to Bayesian Network: A Representation Change Algorithm.
SARA 2005: 107-120 |
| 59 | EE | Yann Semet,
Sylvain Gelly,
Marc Schoenauer,
Michèle Sebag:
Artificial Agents and Speculative Bubbles
CoRR abs/cs/0511093: (2005) |
| 58 | EE | Jérôme Azé,
Mathieu Roche,
Yves Kodratoff,
Michèle Sebag:
Preference Learning in Terminology Extraction: A ROC-based approach
CoRR abs/cs/0512050: (2005) |
| 2004 |
|---|
| 57 | EE | Alexandre Termier,
Marie-Christine Rousset,
Michèle Sebag:
DRYADE: A New Approach for Discovering Closed Frequent Trees in Heterogeneous Tree Databases.
ICDM 2004: 543-546 |
| 56 | EE | Nicolas Baskiotis,
Michèle Sebag:
C4.5 competence map: a phase transition-inspired approach.
ICML 2004 |
| 55 | | Jérôme Azé,
Mathieu Roche,
Yves Kodratoff,
Michèle Sebag:
Learning to Order Terms: Supervised Interestingness Measures in Terminology Extraction
International Conference on Computational Intelligence 2004: 478-481 |
| 54 | EE | Kees Jong,
Jérémie Mary,
Antoine Cornuéjols,
Elena Marchiori,
Michèle Sebag:
Ensemble Feature Ranking.
PKDD 2004: 267-278 |
| 53 | EE | Kees Jong,
Elena Marchiori,
Michèle Sebag:
Ensemble Learning with Evolutionary Computation: Application to Feature Ranking.
PPSN 2004: 1133-1142 |
| 52 | EE | Nicolas Godzik,
Marc Schoenauer,
Michèle Sebag:
Robotics and Multi-agent Systems Robustness in the Long Run: Auto-teaching vs Anticipation in Evolutionary Robotics.
PPSN 2004: 932-941 |
| 51 | | Mathieu Roche,
Jérôme Azé,
Yves Kodratoff,
Michèle Sebag:
Learning Interestingness Measures in Terminology Extraction. A ROC-based approach.
ROCAI 2004: 81-88 |
| 50 | EE | Jérôme Maloberti,
Michèle Sebag:
Fast Theta-Subsumption with Constraint Satisfaction Algorithms.
Machine Learning 55(2): 137-174 (2004) |
| 2003 |
|---|
| 49 | EE | Michèle Sebag,
Jérôme Azé,
Noël Lucas:
ROC-Based Evolutionary Learning: Application to Medical Data Mining.
Artificial Evolution 2003: 384-396 |
| 48 | | Jérôme Azé,
Noël Lucas,
Michèle Sebag:
Fouille de données visuelle et analyse de facteurs de risque médical.
EGC 2003: 183-188 |
| 47 | | Sébastien Jouteau,
Antoine Cornuéjols,
Michèle Sebag,
Philippe Tarroux,
Jean-Sylvain Liénard:
Nouveaux résultats en classification à l'aide d'un codage par motifs fréquents.
EGC 2003: 521-532 |
| 46 | EE | Nicolas Godzik,
Marc Schoenauer,
Michèle Sebag:
Evolving Symbolic Controllers.
EvoWorkshops 2003: 638-650 |
| 45 | EE | Michèle Sebag,
Jérôme Azé,
Noël Lucas:
Impact Studies and Sensitivity Analysis in Medical Data Mining with ROC-based Genetic Learning.
ICDM 2003: 637-640 |
| 44 | EE | Marco Botta,
Attilio Giordana,
Lorenza Saitta,
Michèle Sebag:
Relational Learning as Search in a Critical Region.
Journal of Machine Learning Research 4: 431-463 (2003) |
| 43 | EE | Hendrik Blockeel,
Michèle Sebag:
Scalability and efficiency in multi-relational data mining.
SIGKDD Explorations 5(1): 17-30 (2003) |
| 2002 |
|---|
| 42 | EE | Alexandre Termier,
Marie-Christine Rousset,
Michèle Sebag:
TreeFinder: a First Step towards XML Data Mining.
ICDM 2002: 450-457 |
| 41 | | Jacques Ales Bianchetti,
Céline Rouveirol,
Michèle Sebag:
Constraint-based Learning of Long Relational Concepts.
ICML 2002: 35-42 |
| 40 | EE | Alain Ratle,
Michèle Sebag:
A Novel Approach to Machine Discovery: Genetic Programming and Stochastic Grammars.
ILP 2002: 207-222 |
| 39 | | Hatem Hamda,
François Jouve,
Evelyne Lutton,
Marc Schoenauer,
Michèle Sebag:
Compact Unstructured Representations for Evolutionary Design.
Appl. Intell. 16(2): 139-155 (2002) |
| 2001 |
|---|
| 38 | | Céline Rouveirol,
Michèle Sebag:
Inductive Logic Programming, 11th International Conference, ILP 2001, Strasbourg, France, September 9-11, 2001, Proceedings
Springer 2001 |
| 37 | EE | Alain Ratle,
Michèle Sebag:
Avoiding the Bloat with Stochastic Grammar-Based Genetic Programming.
Artificial Evolution 2001: 255-266 |
| 36 | EE | Jérôme Maloberti,
Michèle Sebag:
Theta-Subsumption in a Constraint Satisfaction Perspective.
ILP 2001: 164-178 |
| 35 | EE | Alexandre Termier,
Michèle Sebag,
Marie-Christine Rousset:
Combining Statistics and Semantics for Word and Document Clustering.
Workshop on Ontology Learning 2001 |
| 34 | EE | Alain Ratle,
Michèle Sebag:
Grammar-guided genetic programming and dimensional consistency: application to non-parametric identification in mechanics.
Appl. Soft Comput. 1(1): 105-118 (2001) |
| 2000 |
|---|
| 33 | | Attilio Giordana,
Lorenza Saitta,
Michèle Sebag,
Marco Botta:
Analyzing Relational Learning in the Phase Transition Framework.
ICML 2000: 311-318 |
| 32 | EE | Attilio Giordana,
Lorenza Saitta,
Michèle Sebag,
Marco Botta:
Can Relational Learning Scale Up?
ISMIS 2000: 31-39 |
| 31 | | Alain Ratle,
Michèle Sebag:
Genetic Programming and Domain Knowledge: Beyond the Limitations of Grammar-Guided Machine Discovery.
PPSN 2000: 211-220 |
| 30 | | Michèle Sebag,
Céline Rouveirol:
Any-time Relational Reasoning: Resource-bounded Induction and Deduction Through Stochastic Matching.
Machine Learning 38(1-2): 41-62 (2000) |
| 1999 |
|---|
| 29 | EE | Marco Botta,
Attilio Giordana,
Lorenza Saitta,
Michèle Sebag:
Relational Learning: Hard Problems and Phase Transitions.
AI*IA 1999: 178-189 |
| 28 | EE | Michèle Sebag:
From first order logic to Nd: a data driven reformulation.
ESANN 1999: 231-236 |
| 27 | | Michèle Sebag:
Constructive Induction: A Version Space-based Approach.
IJCAI 1999: 708-713 |
| 26 | EE | Alejandro Rosete-Suárez,
Alberto Nogueira-Keeling,
Alberto Ochoa-Rodríguez,
Michèle Sebag:
Hacia un Enfoque General del Trazado de Grafos.
Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial 8: 18-26 (1999) |
| 1998 |
|---|
| 25 | | Antoine Ducoulombier,
Michèle Sebag:
Continuous Mimetic Evolution.
ECML 1998: 334-345 |
| 24 | | Michèle Sebag:
A Stochastic Simple Similarity.
ILP 1998: 95-105 |
| 23 | EE | Michèle Sebag,
Antoine Ducoulombier:
Extending Population-Based Incremental Learning to Continuous Search Spaces.
PPSN 1998: 418-427 |
| 22 | | Michèle Sebag,
Marc Schoenauer,
Mathieu Peyral:
Revisiting the Memory of Evolution.
Fundam. Inform. 35(1-4): 125-162 (1998) |
| 21 | | Olivier Gascuel,
Bernadette Bouchon-Meunier,
Gilles Caraux,
Patrick Gallinari,
Alain Guénoche,
Yann Guermeur,
Yves Lechevallier,
Christophe Marsala,
Laurent Miclet,
Jacques Nicolas,
Richard Nock,
Mohammed Ramdani,
Michèle Sebag,
Basavanneppa Tallur,
Gilles Venturini,
Patrick Vitte:
Twelve Numerical, Symbolic and Hybrid Supervised Classification Methods.
IJPRAI 12(4): 517-571 (1998) |
| 1997 |
|---|
| 20 | EE | Mathieu Peyral,
Antoine Ducoulombier,
Caroline Ravise,
Marc Schoenauer,
Michèle Sebag:
Mimetic Evolution.
Artificial Evolution 1997: 81-94 |
| 19 | | Michèle Sebag,
Marc Schoenauer,
Caroline Ravise:
Inductive Learning of Mutation Step-Size in Evolutionary Parameter Optimization.
Evolutionary Programming 1997: 247-261 |
| 18 | | Michèle Sebag,
Marc Schoenauer,
Caroline Ravise:
Toward Civilized Evolution: Developing Inhibitions.
ICGA 1997: 291-298 |
| 17 | | Michèle Sebag,
Céline Rouveirol:
Tractable Induction and Classification in First Order Logic Via Stochastic Matching.
IJCAI (2) 1997: 888-893 |
| 16 | | Michèle Sebag:
Distance Induction in First Order Logic.
ILP 1997: 264-272 |
| 1996 |
|---|
| 15 | | Michèle Sebag,
Caroline Ravise,
Marc Schoenauer:
Controlling Evolution by Means of Machine Learning.
Evolutionary Programming 1996: 57-66 |
| 14 | | Caroline Ravise,
Michèle Sebag:
An Advanced Evolution Should Not Repeat its Past Errors.
ICML 1996: 400-408 |
| 13 | | Michèle Sebag:
Delaying the Choice of Bias: A Disjunctive Version Space Approach.
ICML 1996: 444-452 |
| 12 | | Michèle Sebag,
Céline Rouveirol:
Polynomial-Time Learning in Logic Programming and Constraint Logic Programming.
Inductive Logic Programming Workshop 1996: 105-126 |
| 11 | | Michèle Sebag,
Marc Schoenauer:
Mutation by Imitation in Boolean Evolution Strategies.
PPSN 1996: 356-365 |
| 10 | | Michèle Sebag,
Céline Rouveirol,
Jean-Francois Puget:
Induction of Constraint Logic Programs.
PRICAI Workshops 1996: 148-167 |
| 1995 |
|---|
| 9 | | Caroline Ravise,
Michèle Sebag,
Marc Schoenauer:
Induction-Based Control of Genetic Algorithms.
Artificial Evolution 1995: 100-119 |
| 8 | | Michèle Sebag,
Marc Schoenauer,
Caroline Ravise:
An Induction-based Control for Genetic Algorithms (Extended Abstract).
ECML 1995: 351-355 |
| 1994 |
|---|
| 7 | | Michèle Sebag:
Using Constraints to Building Version Spaces.
ECML 1994: 257-271 |
| 6 | | Michèle Sebag:
A Constraint-based Induction Algorithm in FOL.
ICML 1994: 275-283 |
| 5 | | Michèle Sebag,
Marc Schoenauer:
Controlling Crossover through Inductive Learning.
PPSN 1994: 209-218 |
| 1993 |
|---|
| 4 | | Michèle Sebag,
Marc Schoenauer:
A Rule-Based Similarity Measure.
EWCBR 1993: 119-131 |
| 1992 |
|---|
| 3 | | Michèle Sebag,
Marc Schoenauer:
Learning to Control Inconsistent Knowledge.
ECAI 1992: 479-483 |
| 1991 |
|---|
| 2 | | Michèle Sebag,
Marc Schoenauer:
Using Examples to Refine a Redundant Knowledge Base.
EUROVAV 1991: 227-236 |
| 1990 |
|---|
| 1 | | Marc Schoenauer,
Michèle Sebag:
Incremental Learning of Rules and Meta-rules.
ML 1990: 49-57 |
