Table of contents

  1. Front Matter
  2. Satoru Miyano
    Pages 19-36
  3. Jānis Bārzdiņš, Guntis Bārzdiņš, Kalvis Apsītis, Uğis Sarkans
    Pages 59-72
  4. Klaus P. Jantke, Steffen Lange
    Pages 87-100
  5. Taisuke Sato, Sumitaka Akiba
    Pages 101-110
  6. Yasuhito Mukouchi, Setsuo Arikawa
    Pages 123-136
  7. Rūsiņš Freivalds, Carl H. Smith
    Pages 137-149
  8. Sanjay Jain, Arun Sharma
    Pages 150-163
  9. Juris Viksna
    Pages 164-172
  10. Takashi Moriyama, Masako Sato
    Pages 187-196
  11. Jorge Ricardo Cuellar, Hans Ulrich Simon
    Pages 223-236
  12. Makoto Iwayama, Nitin Indurkhya, Hiroshi Motoda
    Pages 237-250

About these proceedings


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.


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

Bibliographic information

  • DOI
  • 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
  • About this book