[CEUR Workshop P
roceedings] Vol-35

© 2000 for the individual papers by the papers' authors. Copying permitted for private and scientific purposes. Re-publication of material on this page requires permission by the copyright owners.


Inductive Logic Programming
10th International Conference, ILP 2000
Work-in-progress reports

London, July 2000.

Edited by

James Cussens 
Alan Frisch 
Both at Department of Computer Science, University of York, UK

Supported by

Table of Contents

  1. A Formal Framework for Theory Learning Using Description Logics

  2. Alvarez
  3. Learning Term Rewriting Systems from Entailment

  4. Arimura, Sakamoto and Arikawa
  5. A Higher-order Approach to Meta-learning

  6. Bensusan, Giraud-Carrier and Kennedy
  7. Two Advanced Transormations for Improving the Efficiency of an ILP system

  8. Blockeel, Demoen, Janssens, Vandecasteele and Van Laer
  9. A New Declarative Bias for ILP: Construction Modes

  10. Erdem and Flener
  11. Universal Learning of Classes from Sparse and Non-uniform Evidence

  12. Ferri-Ramirez, Hernandez-Orallo and Ramirez-Quintana
  13. Decomposing Probability Distributions on Structured Individuals

  14. Flach and Lachiche
  15. On the Completion of Inverse Entailment for Mutual Recursion and its Application to Self Recursion

  16. Furukawa and Ozaki
  17. A Topological Study of the Upward Refinement Operators in ILP

  18. Gutierrez-Naranjo, Alonso-Jimenez and Borrego-Diaz
  19. Bayesian Logic Programs

  20. Kersting and De Raedt
  21. Bottom-Up Propositionalization

  22. Kramer and Frank
  23. Using an ILP Algorithm to Learn Logic Programs for Reasoning about Actions

  24. Lorenzo and Otero
  25. Selective Inductive Logic Programming

  26. Maclaren
  27. A Proposal for Inductive Learning Agent Using First-Order Logic

  28. Matsui, Inuzuka and Seki
  29. Composition of Biasses of Inductive Logic Programming

  30. Moal and Vrain
  31. An Efficient Hypothesis Search Algorithm Based on Best-Bound Search

  32. Ohara, Babaguchi and Kitahashi
  33. Using Belief Networks to Neutralize Known Dependencies in Conceptual Clustering

  34. Ramon and Dehaspe
  35. Identification in the Limit and Constraint ILP

  36. Richard
  37. Learning First Order Logic Time Series Classifiers

  38. Rodriguez, Alonso and Bostrom
  39. ILP for Automated Telephony

  40. Zelezny, Miksovsky, Stepankova and Zidek

Submitted by James Cussens, 1 November 2000