Table of contents

  1. Front Matter
  2. Yasubumi Sakakibara
    Pages 1-24
  3. Amr F. Fahmy, Robert S. Roos
    Pages 25-40
  4. Takeshi Koshiba, Erkki Mäkinen, Yuji Takada
    Pages 41-54
  5. Hiroki Arimura, Hiroki Ishizaka, Takeshi Shinohara
    Pages 66-79
  6. Luc De Raedt, Wim Van Laer
    Pages 80-94
  7. M. R. K. Krishna Rao
    Pages 95-109
  8. Akira Miyashiro, Eiji Takimoto, Yoshifumi Sakai, Akira Maruoka
    Pages 110-122
  9. John Kececioglu, Ming Li, John Tromp
    Pages 151-152
  10. John Case, Sanjay Jain, Arun Sharma
    Pages 153-168
  11. Frank Stephan
    Pages 185-200
  12. Bala Kalyanasundaram, Mahendran Velauthapillai
    Pages 201-214
  13. Ricard Gavaldà, David Guijarro
    Pages 228-238
  14. Jorge Castro, José L. Balcázar
    Pages 239-248
  15. Carlos Domingo, John Shawe-Taylor
    Pages 249-260

About these proceedings


This book constitutes the refereed proceedings of the 6th International Workshop on Algorithmic Learning Theory, ALT '95, held in Fukuoka, Japan, in October 1995.
The book contains 21 revised full papers selected from 46 submissions together with three invited contributions. It covers all current areas related to algorithmic learning theory, in particular the theory of machine learning, design and analysis of learning algorithms, computational logic aspects, inductive inference, learning via queries, artificial and biologicial neural network learning, pattern recognition, learning by analogy, statistical learning, inductive logic programming, robot learning, and gene analysis.


algorithmic learning theory algorithms learning learning theory logic machine learning programming robot

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 1995
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-60454-9
  • Online ISBN 978-3-540-47470-8
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book