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Inductive Logic Programming

9th International Workshop, ILP-99, Bled, Slovenia, June 24-27, 1999, Proceedings

  • Conference proceedings
  • © 1999

Overview

Editors:
  1. Sašo Džeroski

    1. Department of Intelligent Systems, Jožef Stefan Institute, Ljubljana, Slovenia

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    You can also search for this editor in PubMed   Google Scholar

  2. Peter Flach

    1. Department of Computer Science, University of Bristol, Bristol, UK

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 1634)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: ILP 1999.

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Table of contents (27 papers)

  1. Front Matter

    Pages I-VIII
    Download chapter PDF

  2. I Invited Papers

    1. Probabilistic Relational Models

      • Daphne Koller
      Pages 3-13

    2. Inductive Databases

      • Heikki Mannila
      Pages 14-14

    3. Some Elements of Machine Learning

      • J. R. Quinlan
      Pages 15-18

  3. II Contributed Papers

    1. Refinement Operators Can Be (Weakly) Perfect

      • Liviu Badea, Monica Stanciu
      Pages 21-32

    2. Combining Divide-and-Conquer and Separate-and-Conquer for Efficient and Effective Rule Induction

      • Henrik Boström, Lars Asker
      Pages 33-43

    3. Refining Complete Hypotheses in ILP

      • Ivan Bratko
      Pages 44-55

    4. Acquiring Graphic Design Knowledge with Nonmonotonic Inductive Learning

      • Kazuya Chiba, Hayato Ohwada, Fumio Mizoguchi
      Pages 56-67

    5. Morphosyntactic Tagging of Slovene Using Progol

      • James Cussens, Sašo Džeroski, Tomaž Erjavec
      Pages 68-79

    6. Experiments in Predicting Biodegradability

      • Sašo Džeroski, Hendrik Blockeel, Boris Kompare, Stefan Kramer, Bernhard Pfahringer, Wim Van Laer
      Pages 80-91

    7. 1BC: A First-Order Bayesian Classifier

      • Peter Flach, Nicolas Lachiche
      Pages 92-103

    8. Sorted Downward Refinement: Building Background Knowledge into a Refinement Operator for Inductive Logic Programming

      • Alan M. Frisch
      Pages 104-115

    9. A Strong Complete Schema for Inductive Functional Logic Programming

      • J. Hernández-Orallo, M. J. Ramírez-Quintana
      Pages 116-127

    10. Application of Different Learning Methods to Hungarian Part-of-Speech Tagging

      • Tamás Horváth, Zoltán Alexin, Tibor Gyimóthy, Stefan Wrobel
      Pages 128-139

    11. Combining LAPIS and WordNet for the Learning of LR Parsers with Optimal Semantic Constraints

      • Dimitar Kazakov
      Pages 140-151

    12. Learning Word Segmentation Rules for Tag Prediction

      • Dimitar Kazakov, Suresh Manandhar, Tomaž Erjavec
      Pages 152-161

    13. Approximate ILP Rules by Backpropagation Neural Network: A Result on Thai Character Recognition

      • Boonserm Kijsirikul, Sukree Sinthupinyo
      Pages 162-173

    14. Rule Evaluation Measures: A Unifying View

      • Nada Lavrač, Peter Flach, Blaz Zupan
      Pages 174-185

    15. Improving Part of Speech Disambiguation Rules by Adding Linguistic Knowledge

      • Nikolaj Lindberg, Martin Eineborg
      Pages 186-197

    16. On Sufficient Conditions for Learnability of Logic Programs from Positive Data

      • Eric Martin, Arun Sharma
      Pages 198-209
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Editors and Affiliations

  • Department of Intelligent Systems, Jožef Stefan Institute, Ljubljana, Slovenia

    Sašo Džeroski

  • Department of Computer Science, University of Bristol, Bristol, UK

    Peter Flach

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