Marc Sebban

Assistant Professor, Computer Science


TRIVIA Laboratory
French West Indies and Guiana University

Welcome to my personal WEB page...
Last modified: 05/18/01

Table of Contents :

Short Curriculum Vitae

Marc Sebban (e-mail : msebban@univ-ag.fr)
Born on february 18th 1969 (Lyon, France)
Maried (2 children)

Education

Hobbies

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Research Interests

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Supervision of PhD Thesis

New Models in Data-Mining for Studying Direct Repeat Locus of the Mycobacterium Tuberculosis (in french), Georges Valetudie (beginning: september, 2000). Program in collaboration with the Pasteur Institute of Pointe-a-Pitre.

Table of Contents

Bibliography (Chronologically)


    2001


    Sebban, M., Nock, R. (2001). A Hybrid Filter/Wrapper Approach of Feature Selection using Information Theory, International Journal of Pattern Recognition (PR), 12 pages (accepted paper).

    Sebban, M., Nock, R., and Lallich, S. (2001). Boosting Neighborhood-Based Classifiers, Eighteenth International Conference on Machine Learning (ICML-01), (accepted paper).

    Nock, R., and Sebban, M. (2001). A Bayesian Boosting Theorem, International Journal of Pattern Recognition Letters (PRL), 22(3-4), pp 413-419.

    Sebban, M., and Nock, R. (2001). Improvement of Nearest-Neighbor Classifiers via Support Vector Machines, Fourteenth International Florida Artificial Intelligence Research Symposium Conference (FLAIRS-01), (accepted paper, to appear).

    Nock, R., and Sebban, M. (2001). Advances in Adaptive Prototype Weighting and Selection, International Journal on Articificial Intelligence Tools (IJAIT), 10(1-2), 137-155

    Nock, R., and Sebban, M. (2001). An improved bound on the Finite Sample Risk of the Nearest Neighbor Rule, International Journal of Pattern Recognition Letters (PRL), 22(3-4), pp 407-412.

    Sebban, M., Nock, R., Chauchat, J.H. and Rakotomalala, R. (2000). Impact of Learning Set Quality and Size on Decision Tree Performances, International Journal of Computers, Systems and Signals (IJCSS), 1(1), 85-105. PDF Version.

    Nock, R., and Sebban, M. (2001). Prototype Selection using Boosted Nearest-Neighbors, in Instance Selection and Construction for Data Mining, Motoda H., and Liu, H. Edts, Kluwer Academic Publishers, vol 608, february 2001.



    2000

    Sebban, M., and Nock, R. (2000). Instance Pruning as an Information Preserving Problem, Seventeenth International Conference on Machine Learning (ICML-00), Stanford, USA, Morgan Kauffman, pp 855-862 (Download).

    Nock, R., and Sebban, M. (2000). Sharper Bounds for the Hardness of Prototype and Feature Selection, International Conference on Algorithmic Learning Theory (ALT-00), Springer Verlag LNAI, pp 224-237.

    Sebban, M., and Nock, R. (2000). Combining Feature and Prototype Pruning by Uncertainty Minimization, Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI-00), Stanford, USA, Morgan Kauffman (Download).

    Sebban, M., and Nock, R. (2000). Identifying and Eliminating Irrelevant Instances using Information Theory, Thirteenth Canadian Conference on Artificial Intelligence (AI-00), Springer Verlag LNAI 1822, pp 90-101.

    Nock, R., and Sebban, M. (2000). A Boosting-Based Prototype Weighting and Selection Scheme, Thirteenth International Florida Artificial Intelligence Research Symposium Conference (FLAIRS-00), Orlando, Florida.

    Nock, R., Sebban, M., and Bernard, D. (2000).A Symmetric Nearest-Neighbors Learning Rule, Fifth European Workshop on Case-Based Reasoning (EWCBR-00), Trento, Italy.

    Sebban, M., and Nock, R. (2000). Contribution of Dataset Reduction Techniques to Tree-Simplification and Knowledge Discovery, 4th European Conference On Principles and Practice of Knowledge Discovery in Databases (PKDD-00), Lyon, France, Zighed, Komorowski & Zytkow Edts, Springer Verlag, pp 44-53.



    1999

    Sebban, M., and Venturini, G. (1999). Apprentissage Automatique, Hermes Science Publications Edts (ouvrage collectif), 176 pages.

    Sebban, M., and Nock, R. (1999). Contribution of Boosting in Wrapper Models, 3rd European Conference On Principles and Practice of Knowledge Discovery in Databases (PKDD'99), Prague, Czech Republic, September 15-18, Lecture Notes in Artificial Intelligence 1704, J.M. Zytkow & J. Rauch (Eds), Springer, pp 214-222. Download.

    Nock, R., Sebban, M. and Jappy, P. (1999). Experiments on a Representation-Independent "Top-Down and Prune" Induction Scheme, 3rd European Conference On Principles and Practice of Knowledge Discovery in Databases (PKDD'99), Prague, September 15-18, Lecture Notes in Artificial Intelligence 1704, J.M. Zytkow & J. Rauch (Eds), Springer, pp 223-231.

    Sebban, M., Zighed D.A., and Di Palma, S. (1999). Selection and Statistical Validation of Features and Prototypes, 3rd European Conference On Principles and Practice of Knowledge Discovery in Databases (PKDD'99), Prague, September 15-18, Lecture Notes in Artificial Intelligence 1704, J.M. Zytkow & J. Rauch (Eds), Springer, pp 184-192.

    Sebban, M., and Richard, G. (1999). From Theoretical Learnability to Statistical Measures of the Learnable, In Proceedings of the The Third International Symposium on Intelligent Data Analysis (IDA'99), Amsterdam, The Netherlands, Lecture Notes in Computer Science 1642, Hand, Kok & Berthold (Eds), Springer, pp 3-14. (Download).

    Sebban, M. (1999). On Feature Selection: a New Filter Model, In Proceedings of the 12th International Florida Artificial Intelligence Research Symposium Conference, AAAI Press California Edts, Orlando, Florida, pp 230-234.


    1998


    Sebban, M., Rabaseda, S., and Zighed, D.A. (1998). Construction of a Gait Identification Model by Statistical Learning. Apprentissage, des principes naturels aux méthodes artificielles. Gilbert Ritschard, Andre Berchtold, Francois Duc, Djamel A. Zighed, Editions Hermes, pp139-149.

    Sebban, M., and Lamole, A. (1998). String Clustering and Statistical Validation of Clusters, Advances in Artificial Intelligence, Lecture Notes in Artificial Intelligence, Proceedings, (AI'98) Vol 1418, Robert E. Mercer & Eric Neufeld Eds, Springer Verlag, pp 298-309.

    Sebban, M. (1998). Prototype Selection from Homogeneous Subsets By a Monte Carlo Sampling, In Proceedings of the Eleventh International Florida Artificial Intelligence Research Symposium Conference, D.J. Cook Edts, AAAI Press California, pp 250-253.

    Zighed, D.A., and Sebban, M. (1998). Sélection et Validation Statistique de Variables et de Prototypes, Revue Electronique sur l'Apprentissage par les Données, vol 2, num 1, pp 1-21.



    1997-1996


    Sebban, M., Rabaseda, S., and Zighed, D.A. (1997). Construction of a Gait Identification Model by Statistical Learning. In Proceedings of VIIth Conference on Learning, from natural principles to artificial methods.

    Sebban, M., and Zighed, D.A. (1996). Test de séparabilitédes classes dans Rp. Actes du XXVème colloque des Structures Economiques, Econométrie et Informatique.

    Zighed, D.A., and Sebban, M. (1996). Cognitive model for the identification of odors . In Proceedings of the International IPMU Conference (IPMU'96), pp 699-703.

    Sebban, M., and Zighed, D.A. (1996). Discrimination of odors by semantic attributes and priming effects. In Proceedings of the International IPMU Conference (IPMU'96), pp 693-697.

    Sebban, M., and Zighed, D.A. (1996). GRAF + MIP : Un modèle hybride pour la reconnaissance de formes : application à la reconnaissance des chiffres manuscrits. Actes du Colloque AGI'96, pp 421-424.

    Sebban, M., Rabaseda, S., and Boussaïd, O. (1995). Contribution of related geometrical graphs in pattern contribution. In Proceedings of International Conference on Ordinal and Symbolic Data Analysis (OSDA'95). Springer Verlag, pp 167-178.

    Sebban, M., D. Zighed, Une approche de la séparabilité des classes dans IRp. Actes 4èmes Rencontres de la Société Francophonede Classification.

    S. Rabaseda, Sebban, M. & R. Rakotomalala, A comparison of some contextual discretization methods, Information Sciences 92(1-4): 137-157, 1996.


    1995-1994


    Rabaseda, S., Sebban, M., and Rakotomalala, R. (1995). Discretization of continous attributes: a survey of methods. In Proceedingsof the second Annual Joint Conference on Information Sciences, pp 164-166.

    Sebban, M., and Rabaseda, S. (1995). Utilisation des graphes de voisinage connexes en Reconnaissance de formes : application à la reconnaissance des ondes de Breiman. Actes des 3èmes Rencontres de la Société Francophone de Classification.

    Rabaseda, S., Rakotomalala, R., and Sebban, M. (1995).     Génération automatique de Connaissances par Induction. Actes des 3èmes Rencontres de la Société Francophone de Classification.

    Sebban, M., and Boussaïd, O. (1994).     Propriétés, complexité et choix des structuresde voisinage en Reconnaissance de Formes. Actes des 2ndes Rencontres de la Société Francophone de Classification.

Table of Contents

JOURNALS

Sebban, M., Nock, R. (2001). A Hybrid Filter/Wrapper Approach of Feature Selection using Information Theory, International Journal of Pattern Recognition (PR), 12 pages (accepted paper).

Nock, R., and Sebban, M. (2001). A Bayesian Boosting Theorem, International Journal of Pattern Recognition Letters (PRL), 22(3-4), pp 413-419.

Sebban, M., Nock, R., Chauchat, J.H. and Rakotomalala, R. (2000). Impact of Learning Set Quality and Size on Decision Tree Performances, International Journal of Computers, Systems and Signals (IJCSS), 1(1), 85-105. PDF Version.

Nock, R., and Sebban, M. (2001). Advances in Adaptive Prototype Weighting and Selection, International Journal on Articificial Intelligence Tools (IJAIT), 10(1-2), 137-155.

Nock, R., and Sebban, M. (2001). An improved bound on the Finite Sample Risk of the Nearest Neighbor Rule, International Journal of Pattern Recognition Letters (PRL), 22(3-4), pp 407-412.

Zighed, D.A., and Sebban, M. (1998). Sélection et Validation Statistique de Variables et de Prototypes, Revue Electronique sur l'Apprentissage par les Données, vol 2, num 1, pp 1-21.


Rabaseda, S., Sebban, M. Rakotomalala, R. (1996). A comparison of Some Contextual Discretization Methods, Information Sciences 92(1-4): 137-157.

INTERNATIONAL CONFERENCES

Sebban, M., Nock, R., and Lallich, S. (2001). Boosting Neighborhood-Based Classifiers, Eighteenth International Conference on Machine Learning (ICML-01), (accepted paper).

Sebban, M., and Nock, R. (2001). Improvement of Nearest-Neighbor Classifiers via Support Vector Machines, Fourteenth International Florida Artificial Intelligence Research Symposium Conference (FLAIRS-01), (accepted paper, to appear).

Sebban, M., and Nock, R. (2000). Instance Pruning as an Information Preserving Problem, Seventeenth International Conference on Machine Learning (ICML-00), Stanford, USA, Morgan Kauffman, pp 855-862 (Download).

Nock, R., and Sebban, M. (2000). Sharper Bounds for the Hardness of Prototype and Feature Selection, International Conference on Algorithmic Learning Theory (ALT-00), Springer Verlag LNAI, pp 224-237.

Sebban, M., and Nock, R. (2000). Combining Feature and Prototype Pruning by Uncertainty Minimization, Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI-00), Stanford, USA, Morgan Kauffman (Download).

Sebban, M., and Nock, R. (2000). Identifying and Eliminating Irrelevant Instances using Information Theory, Thirteenth Canadian Conference on Artificial Intelligence (AI-00), Springer Verlag LNAI 1822, pp 90-101.

Nock, R., and Sebban, M. (2000). A Boosting-Based Prototype Weighting and Selection Scheme, Thirteenth International Florida Artificial Intelligence Research Symposium Conference (FLAIRS-00), Orlando, Florida.

Nock, R., Sebban, M., and Bernard, D. (2000).A Symmetric Nearest-Neighbors Learning Rule, Fifth European Workshop on Case-Based Reasoning (EWCBR-00), Trento, Italy.

Sebban, M., and Nock, R. (2000). Contribution of Dataset Reduction Techniques to Tree-Simplification and Knowledge Discovery, 4th European Conference On Principles and Practice of Knowledge Discovery in Databases (PKDD-00), Lyon, France, Zighed, Komorowski & Zytkow Edts, Springer Verlag, pp 44-53.

Sebban, M., and Nock, R. (1999). Contribution of Boosting in Wrapper Models, 3rd European Conference On Principles and Practice of Knowledge Discovery in Databases (PKDD'99), Prague, Czech Republic, September 15-18, Lecture Notes in Artificial Intelligence 1704, J.M. Zytkow & J. Rauch (Eds), Springer, pp 214-222. Download.

Nock, R., Sebban, M. and Jappy, P. (1999). Experiments on a Representation-Independent "Top-Down and Prune" Induction Scheme, 3rd European Conference On Principles and Practice of Knowledge Discovery in Databases (PKDD'99), Prague, September 15-18, Lecture Notes in Artificial Intelligence 1704, J.M. Zytkow & J. Rauch (Eds), Springer, pp 223-231.

Sebban, M., Zighed D.A., and Di Palma, S. (1999). Selection and Statistical Validation of Features and Prototypes, 3rd European Conference On Principles and Practice of Knowledge Discovery in Databases (PKDD'99), Prague, September 15-18, Lecture Notes in Artificial Intelligence 1704, J.M. Zytkow & J. Rauch (Eds), Springer, pp 184-192.

Sebban, M., and Richard, G. (1999). From Theoretical Learnability to Statistical Measures of the Learnable, In Proceedings of the The Third International Symposium on Intelligent Data Analysis (IDA'99), Amsterdam, The Netherlands, Lecture Notes in Computer Science 1642, Hand, Kok & Berthold (Eds), Springer, pp 3-14. (Download).

Sebban, M. (1999). On Feature Selection: a New Filter Model, In Proceedings of the 12th International Florida Artificial Intelligence Research Symposium Conference, AAAI Press California Edts, Orlando, Florida, pp 230-234.

Sebban, M., and Lamole, A. (1998). String Clustering and Statistical Validation of Clusters, Advances in Artificial Intelligence, Lecture Notes in Artificial Intelligence, Proceedings, (AI'98) Vol 1418, Robert E. Mercer & Eric Neufeld Eds, Springer Verlag, pp 298-309.

Sebban, M. (1998). Prototype Selection from Homogeneous Subsets By a Monte Carlo Sampling, In Proceedings of the Eleventh International Florida Artificial Intelligence Research Symposium Conference, D.J. Cook Edts, AAAI Press California, pp 250-253.

Sebban, M., Rabaseda, S., and Zighed, D.A. (1997). Construction of a Gait Identification Model by Statistical Learning. In Proceedings of VIIth Conference on Learning, from natural principles to artificial methods.

Zighed, D.A., and Sebban, M. (1996). Cognitive model for the identification of odors . In Proceedings of the International IPMU Conference (IPMU'96), pp 699-703.

Sebban, M. & D. Zighed, Discrimination of odors by semantic attributes and priming effects. In Proceedings of the International IPMU Conference (IPMU'96), pp 693-697.

Sebban, M., Rabaseda, S., and Boussaïd, O. (1995). Contribution of related geometrical graphs in pattern contribution. In Proceedings of International Conference on Ordinal and Symbolic Data Analysis (OSDA'95). Springer Verlag, pp 167-178.

Rabaseda, S., Sebban, M., and Rakotomalala, R. (1995). Discretization of continous attributes: a survey of methods. In Proceedingsof the second Annual Joint Conference on Information Sciences, pp 164-166.

NATIONAL CONFERENCES

Sebban, M., and Zighed, D.A. (1996). Test de séparabilité des classes dans Rp. Actes du XXVème colloque des Structures Economiques, Econométrie et Informatique.

Sebban, M., and Zighed, D.A. (1996). GRAF + MIP : Un modèle hybride pour la reconnaissance de formes : application à la reconnaissance des chiffres manuscrits. Actes du Colloque AGI'96, pp 421-424.

Sebban, M., D. Zighed, Une approche de la séparabilité des classes dans IRp. Actes 4èmes Rencontres de la Société Francophonede Classification.

Sebban, M., and Rabaseda, S. (1995). Utilisation des graphes de voisinage connexes en Reconnaissance de formes : application à la reconnaissance des ondes de Breiman. Actes des 3èmes Rencontres de la Société Francophone de Classification.

Rabaseda, S., Rakotomalala, R., and Sebban, M. (1995). Génération automatique de Connaissances par Induction. Actes des 3èmes Rencontres de la Société Francophone de Classification.

Sebban, M., and Boussaïd, O. (1994). Propriétés, complexité et choix des structuresde voisinage en Reconnaissance de Formes. Actes des 2ndes Rencontres de la Société Francophone de Classification.

CHAPTER BOOK

Nock, R., and Sebban, M. (2001). Prototype Selection using Boosted Nearest-Neighbors, in Instance Selection and Construction for Data Mining, Motoda H., and Liu, H. Edts, Kluwer Academic Publishers, vol 608, february 2001.

Sebban, M., Rabaseda, S., and Zighed, D.A. (1998). Construction of a Gait Identification Model by Statistical Learning. Apprentissage, des principes naturels aux méthodes artificielles. Gilbert Ritschard, Andre Berchtold, Francois Duc, Djamel A. Zighed, Editions Hermes, pp139-149.

BOOK

Sebban, M., and Venturini, G. (1999). Apprentissage Automatique, Hermes Science Publications Edts (ouvrage collectif), 176 pages.

Contacts

    Marc Sebban (msebban@univ-ag.fr)
    TRIVIA Laboratory
    Juridical and Economic Sciences Department
    French West Indies and Guiana University
    97159 Pointe a Pitre, France
    Fax : 05 90 90 80 38

Table of Contents