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Table of contents

  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
  21. On the VC-dimension of depth four threshold circuits and the complexity of Boolean-valued functions
    Akito Sakurai
    Pages 251-264
  22. On the sample complexity of consistent learning with one-sided error
    Eiji Takimoto, Akira Maruoka
    Pages 265-278
  23. Complexity of computing Vapnik-Chervonenkis dimension
    Ayumi Shinohara
    Pages 279-287
  24. ε-approximations of k-label spaces
    Susumu Hasegawa, Hiroshi Imai, Masaki Ishiguro
    Pages 288-299
  25. Exact learning of linear combinations of monotone terms from function value queries
    Atsuyoshi Nakamura, Naoki Abe
    Pages 300-313
  26. Thue systems and DNA — A learning algorithm for a subclass
    Rani Siromoney, D. G. Thomas, K. G. Subramanian, V. R. Dare
    Pages 314-327
  27. The VC-dimensions of finite automata with n states
    Yoshiyasu Ishigami, Sei'ichi Tani
    Pages 328-341
  28. Unifying learning methods by colored digraphs
    Kenichi Yoshida, Hiroshi Motoda, Nitin Indurkhya
    Pages 342-355
  29. A perceptual criterion for visually controlling learning
    Masaki Suwa, Hiroshi Motoda
    Pages 356-369
  30. Learning strategies using decision lists
    Satoshi Kobayashi
    Pages 370-383
  31. A decomposition based induction model for discovering concept clusters from databases
    Ning Zhong, Setsuo Ohsuga
    Pages 384-397
  32. Algebraic structure of some learning systems
    Jean-Gabriel Ganascia
    Pages 398-409
  33. Induction of probabilistic rules based on rough set theory
    Shusaku Tsumoto, Hiroshi Tanska
    Pages 410-423
  34. Back Matter
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About these proceedings

Introduction

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.

Keywords

algorithm algorithmic learning theory algorithms artificial intelligence intelligence learning learning theory logic machine learning

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-57370-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 1993
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-57370-8
  • Online ISBN 978-3-540-48096-9
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site