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Table of contents

  1. Front Matter
  2. Pages 2-15
  3. Pages 17-34
  4. Pages 55-74
  5. Pages 105-126
  6. Pages 127-159
  7. Pages 197-206
  8. Pages 207-217
  9. Pages 243-263
  10. Pages 265-278
  11. Pages 279-297
  12. Pages 299-320
  13. Pages 321-343
  14. Pages 345-363
  15. Back Matter

About this book

Introduction

Inductive Logic Programming is a young and rapidly growing field combining machine learning and logic programming. This self-contained tutorial is the first theoretical introduction to ILP; it provides the reader with a rigorous and sufficiently broad basis for future research in the area.
In the first part, a thorough treatment of first-order logic, resolution-based theorem proving, and logic programming is given. The second part introduces the main concepts of ILP and systematically develops the most important results on model inference, inverse resolution, unfolding, refinement operators, least generalizations, and ways to deal with background knowledge. Furthermore, the authors give an overview of PAC learning results in ILP and of some of the most relevant implemented systems.

Keywords

Resolution learning logic machine learning programming proving theorem proving

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-62927-0
  • Copyright Information Springer-Verlag Berlin Heidelberg 1997
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-62927-6
  • Online ISBN 978-3-540-69049-8
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site