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Algorithmic Learning Theory

4th International Workshop, ALT '93, Tokyo, Japan, November 8-10, 1993. Proceedings

  • Conference proceedings
  • © 1993

Overview

Editors:
  1. Klaus P. Jantke
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  2. Shigenobu Kobayashi
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  3. Etsuji Tomita
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  4. Takashi Yokomori
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 744)

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

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Table of contents (32 papers)

  1. Front Matter

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  2. Identifying and using patterns in sequential data

    • Philip Laird
    Pages 1-18

  3. Learning theory toward Genome Informatics

    • Satoru Miyano
    Pages 19-36

  4. Optimal layered learning: A PAC approach to incremental sampling

    • Stephen Muggleton
    Pages 37-44

  5. Reformulation of explanation by linear logic toward logic for explanation

    • Jun Arima, Hajime Sawamura
    Pages 45-57

  6. Towards efficient inductive synthesis of expressions from input/output examples

    • Jānis Bārzdiņš, Guntis Bārzdiņš, Kalvis Apsītis, Uğis Sarkans
    Pages 59-72

  7. A typed λ-calculus for proving-by-example and bottom-up generalization procedure

    • Masami Hagiya
    Pages 73-86

  8. Case-based representation and learning of pattern languages

    • Klaus P. Jantke, Steffen Lange
    Pages 87-100

  9. Inductive resolution

    • Taisuke Sato, Sumitaka Akiba
    Pages 101-110

  10. Generalized unification as background knowledge in learning logic programs

    • Akihiro Yamamoto
    Pages 111-122

  11. Inductive inference machines that can refute hypothesis spaces

    • Yasuhito Mukouchi, Setsuo Arikawa
    Pages 123-136

  12. On the duality between mechanistic learners and what it is they learn

    • Rūsiņš Freivalds, Carl H. Smith
    Pages 137-149

  13. On aggregating teams of learning machines

    • Sanjay Jain, Arun Sharma
    Pages 150-163

  14. Learning with growing quality

    • Juris Viksna
    Pages 164-172

  15. Use of reduction arguments in determining Popperian FIN-type learning capabilities

    • Robert Daley, Bala Kalyanasundaram
    Pages 173-186

  16. Properties of language classes with finite elasticity

    • Takashi Moriyama, Masako Sato
    Pages 187-196

  17. Uniform characterizations of various kinds of language learning

    • Shyam Kapur
    Pages 197-208

  18. How to invent characterizable inference methods for regular languages

    • Timo Knuutila
    Pages 209-222

  19. Neural Discriminant Analysis

    • Jorge Ricardo Cuellar, Hans Ulrich Simon
    Pages 223-236

  20. A new algorithm for automatic configuration of Hidden Markov Models

    • Makoto Iwayama, Nitin Indurkhya, Hiroshi Motoda
    Pages 237-250
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About this book

This volume contains all the papers that were presented at the Fourth Workshop on Algorithmic Learning Theory, held in Tokyo in November 1993. In addition to 3 invited papers, 29 papers were selected from 47 submitted extended abstracts. The workshop was the fourth in a series of ALT workshops, whose focus is on theories of machine learning and the application of such theories to real-world learning problems. The ALT workshops have been held annually since 1990, sponsored by the Japanese Society for Artificial Intelligence. The volume is organized into parts on inductive logic and inference, inductive inference, approximate learning, query learning, explanation-based learning, and new learning paradigms.

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