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Algorithmic Learning Theory - ALT '92

Third Workshop, ALT '92, Tokyo, Japan, October 20-22, 1992. Proceedings

  • Conference proceedings
  • © 1993

Overview

Editors:
  1. Shuji Doshita
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  2. Koichi Furukawa
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  3. Klaus P. Jantke
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  4. Toyaki Nishida
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 743)

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

Included in the following conference series:

Conference proceedings info: ALT 1992.

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Table of contents (22 papers)

  1. Front Matter

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  2. Discovery learning in intelligent tutoring systems

    • Setsuko Otsuki
    Pages 1-12

  3. From inductive inference to algorithmic learning theory

    • Rolf Wiehagen
    Pages 13-24

  4. A stochastic approach to genetic information processing

    • Akihiko Konagaya
    Pages 25-36

  5. On learning systolic languages

    • Takashi Yokomori
    Pages 37-52

  6. A note on the query complexity of learning DFA

    • José L. Balcázar, Josep Díaz, Ricard Gavaldà, Osamu Watanabe
    Pages 53-62

  7. Polynomial-time MAT learning of multilinear logic programs

    • Kimihito Ito, Akihiro Yamamoto
    Pages 63-74

  8. Iterative weighted least squares algorithms for neural networks classifiers

    • Takio Kurita
    Pages 75-86

  9. Domains of attraction in autoassociative memory networks for character pattern recognition

    • Koichi Niijima
    Pages 87-98

  10. Regularization learning of neural networks for generalization

    • Shotaro Akaho
    Pages 99-110

  11. Competitive learning by entropy minimization

    • Ryotaro Kamimura
    Pages 111-122

  12. Inductive inference with bounded mind changes

    • Yasuhito Mukouchi
    Pages 123-134

  13. Efficient inductive inference of primitive Prologs from positive data

    • Hiroki Ishizaka, Hiroki Arimura, Takeshi Shinohara
    Pages 135-146

  14. Monotonic language learning

    • Shyam Kapur
    Pages 147-158

  15. Prudence in vacillatory language identification (Extended abstract)

    • Sanjay Jain, Arun Sharma
    Pages 159-168

  16. Implementation of heuristic problem solving process including analogical reasoning

    • Kazuhiro Ueda, Saburo Nagano
    Pages 169-182

  17. Planning with abstraction based on partial predicate mappings

    • Yoshiaki Okubo, Makoto Haraguchi
    Pages 183-194

  18. Learning k-term monotone Boolean formulae

    • Yoshifumi Sakai, Akira Maruoka
    Pages 195-207

  19. Some improved sample complexity bounds in the probabilistic PAC learning model

    • Jun-ichi Takeuchi
    Pages 208-219

  20. An application of Bernstein polynomials in PAC model

    • Masahiro Matsuoka
    Pages 220-228
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About this book

This volume contains the papers that were presented at the Third Workshop onAlgorithmic Learning Theory, held in Tokyo in October 1992. In addition to 3invited papers, the volume contains 19 papers accepted for presentation, selected from 29 submitted extended abstracts. The ALT workshops have been held annually since 1990 and are organized and sponsored by the Japanese Society for Artificial Intelligence. The main objective of these workshops is to provide an open forum for discussions and exchanges of ideasbetween researchers from various backgrounds in this emerging, interdisciplinary field of learning theory. The volume is organized into parts on learning via query, neural networks, inductive inference, analogical reasoning, and approximate learning.

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