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Algorithmic Learning Theory

6th International Workshop, ALT '95, Fukuoka, Japan, October 18 - 20, 1995. Proceedings

  • Conference proceedings
  • © 1995

Overview

Editors:
  1. Klaus P. Jantke
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  2. Takeshi Shinohara
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  3. Thomas Zeugmann
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 997)

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

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Table of contents (24 papers)

  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
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Keywords

About this book

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.

Bibliographic Information

  • Book Title: Algorithmic Learning Theory

  • Book Subtitle: 6th International Workshop, ALT '95, Fukuoka, Japan, October 18 - 20, 1995. Proceedings

  • Editors: Klaus P. Jantke, Takeshi Shinohara, Thomas Zeugmann

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/3-540-60454-5

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Berlin Heidelberg 1995

  • Softcover ISBN: 978-3-540-60454-9Published: 05 October 1995

  • eBook ISBN: 978-3-540-47470-8Published: 13 July 2005

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XV, 324

  • Topics: Artificial Intelligence, Theory of Computation

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