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Table of contents

  1. Front Matter
  2. Leslie G. Valiant
    Pages 1-11
  3. Eiji Takimoto, Yoshifumi Sakai, Akira Maruoka
    Pages 12-25
  4. Atsuyoshi Nakamura
    Pages 37-50
  5. Hans Kleine Büning, Theodor Lettmann
    Pages 51-58
  6. Rohan A. Baxter, Jonathan J. Oliver
    Pages 83-90
  7. Pearson R. A., Smith E. K. T.
    Pages 91-99
  8. V. Arvind, N. V. Vinodchandran
    Pages 100-112
  9. Amr F. Fahmy, Robert S. Roos
    Pages 113-126
  10. Nada Lavrač, Dragan Gamberger, Peter Turney
    Pages 127-134
  11. J. R. Quinlan
    Pages 143-155
  12. Lionel Martin, Christel Vrain
    Pages 169-176
  13. Noriko Sugimoto, Kouichi Hirata, Hiroki Ishizaka
    Pages 177-184
  14. Jianguo Lu, Jun Arima
    Pages 185-198
  15. Dragan Gamberger, Nada Lavrač, Sašo Džeroski
    Pages 199-212
  16. David L. Dowe, Jonathan J. Oliver, Chris S. Wallace
    Pages 213-227
  17. Steffen Lange, Rolf Wiehagen, Thomas Zeugmann
    Pages 228-241
  18. Sanjay Jain, Efim Kinber, Rolf Wiehagen
    Pages 242-255
  19. M. R. K. Krishna Rao
    Pages 272-284
  20. John Case, Sanjay Jain, Frank Stephan
    Pages 285-298
  21. Andris Ambainis, Rūsiņs Freivalds
    Pages 299-311
  22. Back Matter

About these proceedings

Introduction

This book constitutes the refereed proceedings of the 7th International Workshop on Algorithmic Learning Theory, ALT '96, held in Sydney, Australia, in October 1996.
The 16 revised full papers presented were selected from 41 submissions; also included are eight short papers as well as four full length invited contributions by Ross Quinlan, Takeshi Shinohara, Leslie Valiant, and Paul Vitanyi, and an introduction by the volume editors. The book covers all areas related to algorithmic learning theory, ranging from theoretical foundations of machine learning to applications in several areas.

Keywords

Algorithmic Learning Algorithmisches Lernen Computational Learning Inductive Inference Induktive Inferenz Konzeptuelles Lernen Logic Programming Maschinelles Lernen algorithmic learning theory algorithms learning learning theory machine learning

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-61863-5
  • Copyright Information Springer-Verlag Berlin Heidelberg 1996
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-61863-8
  • Online ISBN 978-3-540-70719-6
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site