Richard Nock
Full Professor (Computer Science)

[ Bio etc. ] [ Teaching etc. ] [ Research etc. ] [ Publications etc. ] [ Everything else ]

Contact

I am affiliated with the Faculty of Law and Economics of Martinique of the (French spelled) "Université des Antilles et de la Guyane", which spans the territories of Guadeloupe, Martinique and French Guyana throughout the Caribbean and south America.

I do my research in a nice lab with cool atmosphere, the CEREGMIA. Visit us if you wish to check for yourself !

 (Way too brief) Bio

I was appointed as Full professor in 2004. Before that, I obtained my accreditation to lead research (HDR) in 2002 and a PhD in Computer Science in 1998. I was previously associate professor (2002-04), and assistant professor (1998-02). Before that, I did a scientific conscription here with a joint contract in 1996-97. In another life, I got my "Ingénieur Agro" degree (MSc in Agronomical Engineering Sciences, majoring in industrial microbiology and biostatistics...) and a MSc in Computer Science (both in 1993, both magna cum laude). Before that, I did my "Classes Préparatoires" (Competitive entrance to French Engineering Schools) in 1988-90. Long before, I was born in the early 70's, at the same age my little angel was born.

 Teaching etc.

 Research etc.

Overview

 Fundings *

 Invitations (visiting grants) **

Others

 (Most of my) Publications

     [ Invited Talks (published) ] ( Journals: below ) ( Conferences: below )

  1. Richard Nock and Frank Nielsen
    Intrinsic Geometries in Learning
    Emerging Trends in Visual Computing (Ecole Polytechnique, 11/18-20/08)
    pp 175-215, 2009. Springer Verlag LNCS 5416.
    Related material (paper): here.
     

     [ Journals ] ( Invited Talks (published): above ) ( Conferences: below )

  2. Frank Nielsen and Richard Nock
    Sided and Symmetrized Bregman Centroids
    IEEE Transactions on Information Theory
    accepted, 2008. IEEE CS Press.
    Related material (paper): here.
     
  3. Richard Nock and Frank Nielsen
    Bregman Divergences and Surrogates for Learning
    IEEE Transactions on Pattern Analysis and Machine Intelligence
    accepted, 2008. IEEE CS Press.
    Related material (paper): here.
     
  4. Natalia Polouliakh, Richard Nock, Frank Nielsen and Hiroaki Kitano
    G-Protein Coupled Receptor Signaling Architecture of Mammalian Immune Cells
    PLoS ONE
    accepted, 2008. Public Library of Sciences.
    Related material (paper): here.
     
  5. Richard Nock, Pascal Vaillant, Claudia Henry and Frank Nielsen
    Soft Memberships for Spectral Clustering, with Application to Permeable Language Distinction
    Pattern Recognition
    accepted, 2008. Elsevier.
    Related material (paper): here.
     
  6. Frank Nielsen and Richard Nock
    Approximating Smallest Enclosing Balls with Application to Machine Learning
    International Journal on Computational Geometry and Applications
    accepted, 2008. World Scientific Publishing.
    Related material (paper): here.
     
  7. Frank Nielsen and Richard Nock
    On the Smallest Enclosing Information Disk
    Information Processing Letters
    (105)-3, pp 93-97, 2008. Elsevier.
    Related material (paper): here.
     
  8. Richard Nock and Frank Nielsen
    Self-Improved gaps Almost Everywhere for the Agnostic Approximation of Monomials
    Theoretical Computer Science (A)
    (377)-1-3, pp 139-150, 2007. Elsevier.
    Related material (paper): here.
     
  9. Richard Nock and Frank Nielsen
    A eal Generalization of discrete AdaBoost
    Artificial Intelligence
    (171)-1, pp 25-41, 2007. Elsevier.
    Related material (paper): here.
     
  10. Pierre-Alain Laur, Richard Nock, Jean-Emile Symphor and Pascal Poncelet
    Mining Evolving Data Streams for Frequent Patterns
    Pattern Recognition
    (40)-2, pp 492-503, 2007. Elsevier.
    Related material (paper): here.
     
  11. Pierre-Alain Laur, Jean-Emile Symphor, Richard Nock and Pascal Poncelet
    Statistical Supports for Mining Sequential Patterns and Improving the Incremental Update Process on Data Streams
    International Journal on Intelligent Data Analysis
    (11)-1, pp 29-47, 2007. IOS press.
    Related material (paper): here.
     
  12. Richard Nock and Frank Nielsen
    On Weighting Clustering
    IEEE Transactions on Pattern Analysis and Machine Intelligence
    (28)-8, pp 1223-1235, 2006. IEEE CS press.
    Related material (paper): here.
    Related material (page with binaries, images, etc.): here.
     
  13. Richard Nock and Frank Nielsen
    Semi-supervised Statistical Region Refinement for Color Image Segmentation
    Pattern Recognition
    (38)-6, pp 835-846, 2005. Elsevier.
    Related material (paper): here.
     
  14. Frank Nielsen and Richard Nock
    A Fast Deterministic Smallest Enclosing Disk Approximation Algorithm
    Information Processing Letters
    (93)-6, pp 263-268, 2005. Elsevier.
    Related material (paper): here.
     
  15. Richard Nock and Frank Nielsen
    Statistical Region Merging
    IEEE Transactions on Pattern Analysis and Machine Intelligence
    (26)-11, pp 1452-1458, 2004. IEEE CS press.
    Related material (paper): here.
    Related material (page with binaries, images, etc.): here.
     
  16. Richard Nock and Frank Nielsen
    On Domain-Partitioning Induction Criteria: Worst-case Bounds for the Worst-case Based
    Theoretical Computer Science (A)
    (321), pp 371-382, 2004. Elsevier.
    Related material (paper): here.
     
  17. Richard Nock
    Complexity in the Case against Accuracy Estimation
    Theoretical Computer Science (A)
    (301), pp 143-165, 2003. Elsevier.
    Related material (paper): here.
     
  18. Richard Nock, Marc Sebban and Didier Bernard
    A Simple locally Adaptive Nearest Neighbor Rule with Application to Pollution Forecasting
    International Journal on Pattern Recognition and Artificial Intelligence
    (17)-8, pp 1-14, 2003. World Scientific Publishing.
    Related material (paper): here.
     
  19. Richard Nock, Tapio Elomaa and Matti Kääriäinen
    Reduced Error Pruning of Branching Programs cannot be Approximated to within a Logarithmic Factor
    Information Processing Letters
    (87), pp 73-78, 2003. Elsevier.
    Related material (paper): here.
     
  20. Marc Sebban, Richard Nock and Stéphane Lallich
    Stopping Criterion for Boosting-based Data Reduction Techniques: from Binary to Multiclass Problems
    Journal of Machine Learning Research
    (3), pp 863-885, 2002. MIT Press.
    Related material (paper): here.
     
  21. Richard Nock
    Inducing Interpretable Voting Classifiers without trading Accuracy for Simplicity: Theoretical Results, Approximation Algorithms, and Experiments
    Journal of Artificial Intelligence Research
    (17), pp 137-170, 2002. Morgan Kauffman.
    Related material (paper): here.
     
  22. Marc Sebban and Richard Nock
    A Hybrid Filter/Wrapper Approach of Feature Selection Using Information Theory
    Pattern Recognition
    (35) 4, pp 835-846, 2002. Elsevier.
    Related material (paper): here.
     
  23. Richard Nock and Marc Sebban
    Advances in Adaptive Prototype Weighting and Selection
    Artificial Intelligence Tools
    (10), pp 137-156, 2001. World Scientific Pub..
     
  24. Marc Sebban, Richard Nock, Jean-Hugues Chauchat and Ricco Rakotomalala
    Impact of Learning Set Quality and Size on Decision Tree Performances
    International Journal of Computers, Systems and Signals
    pp 85-105, 2001. IAAMSAD Publishing.
     
  25. Richard Nock and Marc Sebban
    An improved bound on the Finite Sample Risk of the Nearest Neighbor Rule
    Pattern Recognition Letters
    (22) 3-4. pp 413-419, 2001. Elsevier.
    Related material (paper): here.
     
  26. Richard Nock and Marc Sebban
    A Bayesian Boosting Theorem
    Pattern Recognition Letters
    (22) 3-4. pp 407-412, 2001. Elsevier.
    Related material (paper): here.
     
  27. Richard Nock and Pascal Jappy
    Decision Tree based induction of Decision Lists
    International Journal on Intelligent Data Analysis
    (3) 3. pp 227-240, 1999. Elsevier-IOS Press.
     
  28. Olivier Gascuel, Richard Nock et al. (SYMENU Group)
    Twelve Numerical, Symbolic and Hybrid Supervised Classification Methods
    International Journal of Pattern Recognition and Artificial Intelligence
    pp 517-572, 1998. World Scientific Publishing.
     

    Book Chapters

  29. Richard Nock and Marc Sebban
    Prototype Selection using Boosted Nearest-Neighbors
    Chapter of the book "Instance Selection and Construction for Data Mining" (Ed. H. Liu and H Motoda)
    pp 301-318, 2001. Kluwer Academic Publishers.
     

     [ Conferences ] ( Invited Talks (published): above ) ( Journals: above )

  30. Richard Nock and Frank Nielsen
    On the Efficient Minimization of Classification-Calibrated Surrogates
    NIPS*21 - Advances in Neural Information Processing Systems (Vancouver, Canada)
    accepted (spotlight), 2008. MIT Press.
    Related material (paper): here.
     
  31. Richard Nock, Panu Luosto and Jyrki Kivinen
    Mixed Bregman Clustering with Approximation Guarantees
    ECML'08 - European Conference on Machine Learning (Antwerp, Belgium)
    accepted, 2008. Springer Verlag LNCS.
     
  32. Frank Nielsen and Richard Nock
    Quantum Voronoi Diagrams and Holevo Channel Capacity for 1-Qubit Quantum States
    ISIT'08 - IEEE International Symposium on Information Theory (Toronto, Canada)
    accepted, 2008. IEEE CS Press.
     
  33. Natalia Polouliakh, Yukiko Matsuoka, Samik Ghosh, Richard Nock, Frank Nielsen, Satoshi Kitajima, Atsuya Takagi, Ken-Ichi Aisaki, Jun Kanno and Hiroaki Kitano
    Signaling Network in Mouse Embryonic Stem Cells
    ISCB'08 - International Conference on Systems Biology (Göteborg, Sweden)
    accepted, 2008 (poster).
     
  34. Claudia Henry, Richard Nock and Frank Nielsen
    eal Boosting à la carte with an application to Boosting Oblique Decision Trees
    IJCAI'07 - International Joint Conference on Artificial Intelligence (Hyderabad, India)
    pp 842-847 (oral), 2007. Morgan Kaufmann.
    Related material (paper): here.
     
  35. Frank Nielsen, Jean-Daniel Boissonnat and Richard Nock
    On Bregman Voronoi Diagrams
    SODA'07 - ACM/SIAM International Symposium on Discrete Algorithms (New Orleans, USA)
    pp 746-755, 2007. ACM press.
     
  36. Frank Nielsen, Jean-Daniel Boissonnat and Richard Nock
    Visualizing Bregman Voronoi Diagrams
    SoCG'07 - ACM International Symposium on Computational Geometry (Gyeongju, Korea)
    pp 121-122, 2007. ACM press.
     
  37. Frank Nielsen and Richard Nock
    Fast Graph Segmentation based on Statistical Aggregation Phenomena
    MVA'07 - IAPR International Conference on Machine-Vision Applications (Tokyo, Japan)
    pp 150-153, 2007. IAPR press.
     
  38. Richard Nock and Frank Nielsen
    A eal Generalization of discrete AdaBoost
    ECAI'06 - European Conference on Artificial Intelligence (Riva del Gardia, Italy)
    pp 509-515, 2006. IOS press.
    Best Paper Award.
     
  39. Richard Nock, Pascal Vaillant, Frank Nielsen and Claudia Henry
    Soft Uncoupling of Markov chains for Permeable Language Distinction: A New Algorithm
    ECAI'06 - European Conference on Artificial Intelligence (Riva del Gardia, Italy)
    pp 823-824, 2006. IOS press.
     
  40. Frank Nielsen and Richard Nock
    On Approximating the Smallest Enclosing Bregman Balls
    SoCG'06 - ACM International Symposium on Computational Geometry (Sedona, USA)
    pp 485-486, 2006. ACM press.
     
  41. Richard Nock, Pierre-Alain Laur and Jean-Emile Symphor
    Statistical Borders for Incremental Mining
    ICPR'06 - International Conference on Pattern Recognition (Hong-Kong, China)
    pp 212-215, 2006. IAPR / IEEE press.
     
  42. Patrice Lefaucheur and Richard Nock
    Robust Multiclass Ensemble Classifiers via Symmetric Functions
    ICPR'06 - International Conference on Pattern Recognition (Hong-Kong, China)
    pp 136-139, 2006. IAPR / IEEE press.
     
  43. Svetlana Kiritchenko, Stan Matwin, Richard Nock and Fazel Famili
    Learning and Evaluation in the Presence of Class Hierarchies: Application to Text Categorization
    AI'06 - Canadian Artificial Intelligence Conference (Québec, Canada)
    pp 395-406, 2006. Springer Verlag LNCS 4013.
     
  44. Frank Nielsen and Richard Nock
    On the Smallest Enclosing Information Disk
    CCCG'06 - Canadian Conference on Computational Geometry (Kingston, Canada)
    pp 131-134, 2006.
     
  45. Frank Nielsen and Richard Nock
    ClickRemoval: interactive pinpoint image object removal
    MM'05 - ACM Multimedia Conference (Singapore)
    pp 315-318, 2005. ACM press.
     
  46. Frank Nielsen and Richard Nock
    Interactive Point-and-Click Segmentation for Object Removal in Digital Images
    HCI'05 - IEEE International Conference on Human-Computer Interaction (Beijing, China)
    pp 131-140, 2005. Springer Verlag LNCS 3766.
     
  47. Pierre-Alain Laur, Richard Nock, Jean-Emile Symphor and Pascal Poncelet
    On the Estimation of Frequent Itemsets for Data Streams: Theory and Experiments
    CIKM'05 - ACM International Conference on Information and Knowledge Management (Bremen, Allemagne)
    pp 327-328, 2005. ACM Press.
     
  48. Richard Nock and Babak Esfandiari
    Adaptive Filtering of Web Pages
    PKDD'05 - European Conference on the Principle and Practice of Knowledge Discovery in Data Bases (Porto, Portugal)
    pp 634-642, 2005. Springer Verlag LNCS 3721.
     
  49. Richard Nock and Frank Nielsen
    Fitting the Smallest Enclosing Bregman Ball
    ECML'05 - European Conference on Machine Learning (Porto, Portugal)
    pp 649-656, 2005. Springer Verlag LNCS 3720.
     
  50. Frank Nielsen and Richard Nock
    Interactive Pinpoint Image Object Removal
    CVPR'05 - IEEE International Conference on Computer Vision and Pattern Recognition (San Diego, USA)
    pp 1191 (plus video), 2005. IEEE CS press.
     
  51. Babak Esfandiari and Richard Nock
    Adaptive Filtering of Advertisements on Web Pages
    WWW'05 - International World Wide Web Conference (Chiba, Japan)
    pp 916-917, 2005. ACM press.
     
  52. Pierre-Alain Laur, Jean-Emile Symphor, Richard Nock and Pascal Poncelet
    Statistical Supports for Frequent Itemsets on Data Streams
    MLDM'05 - IAPR International Conference on Machine Learning and Data Mining in Pattern Recognition (Leipzig, Allemagne)
    pp 395-404, 2005. Springer Verlag LNCS 3587.
     
  53. Jean-Christophe Janodet, Richard Nock, Marc Sebban and Henri-Maxime Suchier
    Boosting Grammatical Inference with Confidence Oracles
    ICML'04 - International Conference on Machine Learning (Banff, Canada)
    pp 425-432, 2004. Morgan Kaufmann / ACM Press.
    Related material (paper): here.
     
  54. Richard Nock and Frank Nielsen
    Grouping with Bias Revisited
    CVPR'04 - IEEE International Conference on Computer Vision and Pattern Recognition (Washington DC, USA)
    pp 460-465, 2004. IEEE CS press.
    Related material (paper): here.
    Related material (page with binaries, images, etc.): here.
     
  55. Frank Nielsen and Richard Nock
    Approximating Smallest Enclosing Balls
    ICCSA'04 - International Conference on Computational Science and its Applications /
    International Workshop on Computational Geometry and Applications (Perugia, Italy)
    pp 147-157, 2004. Springer Verlag LNCS 3045.
     
  56. Richard Nock and Frank Nielsen
    An abstract Weighting Framework for Clustering Algorithms
    SDM'04 - SIAM International Conference on Data Mining (Orlando, FL, USA)
    pp 200-209, 2004. SIAM press.
    Related material (paper): here.
    Related material (slides of the oral presentation): here.
     
  57. Richard Nock and Frank Nielsen
    Improving Clustering Algorithms through Constrained Convex Optimization
    ICPR'04 - International Conference on Pattern Recognition (Cambridge, England)
    pp 557-560, 2004. IAPR / IEEE press.
     
  58. Richard Nock and Vincent Pagé
    Grouping with Bias as Distribution-free Mixture Model Estimation
    ICPR'04 - International Conference on Pattern Recognition (Cambridge, England)
    pp 44-47, 2004. IAPR / IEEE press.
     
  59. Frank Nielsen and Richard Nock
    Approximating Smallest Enclosing Disks
    CCCG'04 - Canadian Conference on Computational Geometry (Montreal, Canada)
    pp 124-127, 2004.
    Related material (paper): here.
     
  60. Frank Nielsen and Richard Nock
    Small(est) enclosing Balls in Unbounded Dimensions
    JCDCG'04 - Japanese Conference on Discrete and Computational Geometry (Tokyo, Japan)
    accepted, 2004.
     
  61. Frank Nielsen and Richard Nock
    On Region Merging: the Statistical Soundness of Fast Sorting, with Applications
    CVPR'03 - IEEE International Conference on Computer Vision and Pattern Recognition (Madison, WI, USA)
    pp 19-26, 2003. IEEE CS press.
    Related material (paper): here.
     
  62. Richard Nock and Patrice Lefaucheur
    A Robust Boosting Algorithm
    ECML'02 - European Conference on Machine Learning (Helsinki, FI)
    pp 319-330, 2002. Springer Verlag LNCS 2430.
     
  63. Richard Nock
    A Fast, Reliable Region-Merging like Approach Handling Occlusions
    ICIP'02 - IEEE International Conference on Image Processing (Rochester, NY, USA)
    accepted, 2002. IEEE SP Press.
     
  64. Richard Nock
    Improving Noise Handling in Probabilistic Sorted Color Region Merging
    ICASSP'02 - IEEE International Conference on Acoustics, Speech and Signal Processing (Orlando, FL, USA)
    accepted, 2002. IEEE SP Press.
     
  65. Richard Nock
    Fast and Reliable Color Region Merging inspired by Decision Tree Pruning
    CVPR'01 - IEEE International Conference on Computer Vision and Pattern Recognition (Kauai, HI, USA)
    pp 271-276, 2001. IEEE CS press.
    Related material (paper): here.
    Related material (slides of the oral presentation): here.
    Related material (page with binaries, images, etc.): here.
     
  66. Marc Sebban, Richard Nock and Stéphane Lallich
    Boosting Neighborhood-Based Classifiers
    ICML'01 - International Conference on Machine Learning (Williamstown, MA, USA)
    pp 505-512, 2001. Morgan Kauffman.
     
  67. Marc Sebban and Richard Nock
    Improvement of Nearest-Neighbor Classifiers via Support Vector Machines
    FLAIRS'01 - International FLAIRS Symposium (Key West, FL, USA)
    pp 113-117, 2001. AAAI Press.
     
  68. Marc Sebban and Richard Nock
    Prototype Selection as an Information-preserving problem
    ICML'00 - International Conference on Machine Learning (Stanford, CA, USA)
    pp 855-862, 2000. Morgan Kauffman.
     
  69. Marc Sebban and Richard Nock
    Combining Feature and Prototype Pruning by Uncertainty Minimization
    UAI'00 - International Conference on Uncertainty in Artificial Intelligence (Stanford, CA, USA)
    pp 533-540, 2000. Morgan Kauffman.
     
  70. Christophe Fiorio and Richard Nock
    Sorted Region Merging to Maximize Test Reliability
    ICIP'00 - IEEE International Conference on Image Processing (Vancouver, Canada)
    pp 808-811, 2000. IEEE SP Press.
     
  71. Richard Nock and Marc Sebban
    Sharper Bounds for the Hardness of Prototype and Feature Selection
    ALT'00 - International Conference on Algorithmic Learning Theory (Sydney, Australia)
    pp 224-237, 2000. Springer Verlag LNCS 1968.
     
  72. Marc Sebban and Richard Nock
    Contribution of Dataset Reduction Techniques to Tree-Simplification and Knowledge Discovery
    PKDD'00 - European Conference on Principles and Practice of KDD (Lyon, France)
    pp 44-53, 2000. Springer Verlag LNAI 1910.
     
  73. Richard Nock, Marc Sebban and Didier Bernard
    A Symmetric Nearest-Neighbor Learning Rule
    EWCBR2k - European Workshop on Case-Based Reasoning (Trento, Italy)
    pp 222-233, 2000. Springer Verlag LNCS 1898.
     
  74. Christophe Fiorio and Richard Nock
    A Concentration-Based Adaptive Approach to Region Merging of Optimal Time and Space Complexities
    BMVC'00 - British Machine Vision Conference (Bristol, England)
    pp 775-784, 2000. SPR/IEE.
     
  75. Marc Sebban and Richard Nock
    Prototype Selection based on Information Theory
    AI'00 - Canadian Artificial Intelligence Conference (Montreal, Canada)
    pp 90-101, 2000. Springer Verlag LNCS 1822.
     
  76. Richard Nock and Marc Sebban
    A New Prototype Weighting and Selection Scheme for Instance Based Learning Algorithms
    FLAIRS'00 - International FLAIRS Symposium (Orlando, FL, USA)
    pp 71-75, 2000. AAAI Press.
     
  77. Richard Nock
    Complexity in the Case against Accuracy: when Building one Function-Free Horn Clause is as Hard as Any
    ALT'99 - International Conference on Algorithmic Learning Theory (Tokyo, Japan)
    pp 182-193, 1999. Springer Verlag LNAI 1720.
     
  78. Richard Nock, Marc Sebban and Pascal Jappy
    Experiments on a Representation-Independent "Top-down and Prune" Induction Scheme
    PKDD'99 - European Conference on Principles and Practice of KDD (Prague, Czech Republic)
    pp 223-231 (long paper), 1999. Springer Verlag LNAI 1704.
     
  79. Marc Sebban and Richard Nock
    Contribution of Boosting in Wrapper Models
    PKDD'99 - European Conference on Principles and Practice of KDD (Prague, Czech Republic)
    pp 214-222 (long paper), 1999. Springer Verlag LNAI 1704.
     
  80. Richard Nock and Pascal Jappy
    A "Top-down and Prune" Induction Scheme for constrained Decision Committees
    IDA'99 - International Symposium on Intelligent Data Analysis (Amsterdam, the Netherlands)
    pp 27-38 (long paper), 1999. Springer Verlag LNCS 1642.
     
  81. Christophe Fiorio and Richard Nock
    Image segmentation using a Generic, Fast and Non-parametric approach
    ICTAI'98 - IEEE International Conference on Tools with Artificial Intelligence (Taipei, Taiwan, ROC)
    pp 450-458, 1998. IEEE CS Press.
     
  82. Richard Nock, Pascal Jappy and Jean Sallantin
    Generalized Graph Colorability and Compressibility of Boolean Formulae
    ISAAC'98 - International Symposium on Algorithms and Computation (Taejon, Korea)
    pp 237-246, 1998. Springer Verlag LNAI 1533.
     
  83. Richard Nock and Pascal Jappy
    Function-free Horn clauses are Hard to approximate
    ILP'98 - International Conference on Inductive Logic Programming (Madison, WI, USA)
    pp 195-204, 1998. Springer Verlag LNAI 1446.
     
  84. Richard Nock and Pascal Jappy
    On the power of Decision Lists
    ICML'98 International Conference on Machine Learning (Madison, WI, USA)
    pp 413-420, 1998. Morgan Kaufmann.
     
  85. Pascal Jappy and Richard Nock
    PAC-learning Conceptual Graphs
    ICCS'98 - International Conference on Conceptual Structures (Montpellier, France)
    pp 303-315, 1998. Springer Verlag LNCS 1453.
     
  86. Richard Nock and Babak Esfandiari
    Oracles and assistants : machine learning applied to network supervision
    AI'98 - Canadian Artificial Intelligence Conference (Vancouver, Canada)
    pp 86-98, 1998. Springer Verlag LNCS 1418.
     
  87. Joel Quinqueton, Babak Esfandiari and Richard Nock
    Chronicle learning and Agent-Oriented techniques for network management and supervision
    IC2IN'97 - International Conference on Intelligent Networks and Intelligence in Networks (Paris, France)
    Invited paper
    pp 131-146, 1997. Chapman & Hall.
     
  88. Pascal Jappy, Richard Nock and Olivier Gascuel
    Negative Robust Learning Results for Horn clause programs
    ICML'96 - International Conference on Machine Learning (Bari, Italie)
    pp 258-265, 1996. Morgan Kaufmann.
     
  89. Richard Nock and Olivier Gascuel
    On learning Decision Committees
    ICML'95 - International Conference on Machine Learning (Tahoe City, CA, USA)
    pp 413-420, 1995. Morgan Kaufmann.

 Copyright notice:

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to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors
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invoked by each author's copyright. Publisher's copyright policy are available at © SIAM, © ACM, © IEEE, © Springer,
© Elsevier.

 Everything else

Last update: January 10th, 2009.

 (*) total exceeds 600 000 ; list does not include fundings in which I am a member but with a minor role.
 (**) 4-8 weeks each.
 (***) "We do not leave childhood. We bury it deep in our self." (Patrick Chamoiseau, in Une enfance Créole I, Antan d'enfance, pp 93, Folio 2844, 1993).