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Table of contents

  1. Front Matter
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  2. Grammatical inference: An old and new paradigm
    Yasubumi Sakakibara
    Pages 1-24
  3. Efficient learning of real time one-counter automata
    Amr F. Fahmy, Robert S. Roos
    Pages 25-40
  4. Learning strongly deterministic even linear languages from positive examples
    Takeshi Koshiba, Erkki Mäkinen, Yuji Takada
    Pages 41-54
  5. Language learning from membership queries and characteristic examples
    Hiroshi Sakamoto
    Pages 55-65
  6. Learning unions of tree patterns using queries
    Hiroki Arimura, Hiroki Ishizaka, Takeshi Shinohara
    Pages 66-79
  7. Inductive constraint logic
    Luc De Raedt, Wim Van Laer
    Pages 80-94
  8. Incremental learning of logic programs
    M. R. K. Krishna Rao
    Pages 95-109
  9. Learning orthogonal F-Horn formulas
    Akira Miyashiro, Eiji Takimoto, Yoshifumi Sakai, Akira Maruoka
    Pages 110-122
  10. Learning nested differences in the presence of malicious noise
    Peter Auer
    Pages 123-137
  11. Learning sparse linear combinations of basis functions over a finite domain
    Atsuyoshi Nakamura, Shinji Miura
    Pages 138-150
  12. Inferring a DNA sequence from erroneous copies (abstract)
    John Kececioglu, Ming Li, John Tromp
    Pages 151-152
  13. Machine induction without revolutionary paradigm shifts
    John Case, Sanjay Jain, Arun Sharma
    Pages 153-168
  14. Probabilistic language learning under monotonicity constraints
    Léa Meyer
    Pages 169-184
  15. Noisy inference and oracles
    Frank Stephan
    Pages 185-200
  16. Simulating teams with many conjectures
    Bala Kalyanasundaram, Mahendran Velauthapillai
    Pages 201-214
  17. Complexity of network training for classes of Neural Networks
    Charles C. Pinter
    Pages 215-227
  18. Learning ordered binary decision diagrams
    Ricard Gavaldà, David Guijarro
    Pages 228-238
  19. Simple PAC learning of simple decision lists
    Jorge Castro, José L. Balcázar
    Pages 239-248
  20. The complexity of learning minor closed graph classes
    Carlos Domingo, John Shawe-Taylor
    Pages 249-260
  21. Technical and scientific issues of KDD (or: Is KDD a science?)
    Yves Kodratoff
    Pages 261-265
  22. Analogical logic program synthesis algorithm that can refute inappropriate similarities
    Ken Sadohara, Makoto Haraguchi
    Pages 266-281
  23. Reflecting and self-confident inductive inference machines
    Klaus P. Jantke
    Pages 282-297
  24. On approximately identifying concept classes in the limit
    Satoshi Kobayashi, Takashi Yokomori
    Pages 298-312
  25. Application of kolmogorov complexity to inductive inference with limited memory
    Andris Ambainis
    Pages 313-318
  26. Back Matter
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About these proceedings

Introduction

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.

Keywords

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

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-60454-5
  • 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
  • Buy this book on publisher's site