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Algorithmic Learning Theory

16th International Conference, ALT 2005, Singapore, October 8-11, 2005. Proceedings

  • Sanjay Jain
  • Hans Ulrich Simon
  • Etsuji Tomita

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3734)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 3734)

Table of contents

  1. Front Matter
  2. Editors’ Introduction

    1. Sanjay Jain, Hans Ulrich Simon, Etsuji Tomita
      Pages 1-9
  3. Invited Papers

    1. Gary Bradshaw
      Pages 10-10
    2. Neil R. Smalheiser
      Pages 11-11
    3. Ross D. King
      Pages 12-12
    4. Pai-Hsuen Chen, Rong-En Fan, Chih-Jen Lin
      Pages 45-62
  4. Kernel-Based Learning

    1. Regular Contributions

      1. Arthur Gretton, Olivier Bousquet, Alex Smola, Bernhard Schölkopf
        Pages 63-77
    2. Bayesian and Statistical Models

      1. Yang-Bo He, Zhi Geng, Xun Liang
        Pages 92-106
    3. PAC-Learning

      1. Omri Guttman, S. V. N. Vishwanathan, Robert C. Williamson
        Pages 171-182
    4. Query-Learning

      1. Rotem Bennet, Nader H. Bshouty
        Pages 183-197
      2. Hirotaka Kato, Satoshi Matsumoto, Tetsuhiro Miyahara
        Pages 211-225
      3. Sanjay Jain, Steffen Lange, Sandra Zilles
        Pages 226-240
    5. Inductive Inference

      1. Lorenzo Carlucci, John Case, Sanjay Jain, Frank Stephan
        Pages 241-255
      2. Sanjay Jain, Efim Kinber
        Pages 256-268
    6. Language Learning

      1. Yen Kaow Ng, Takeshi Shinohara
        Pages 269-282
      2. Alexander Clark, Rémi Eyraud
        Pages 283-296
      3. Henning Fernau
        Pages 297-311
    7. Learning and Logic

    8. Learning from Expert Advice

      1. Shigeaki Harada, Eiji Takimoto, Akira Maruoka
        Pages 343-355
      2. Jan Poland, Marcus Hutter
        Pages 356-370
      3. Jussi Kujala, Tapio Elomaa
        Pages 371-385
      4. Matthew Henderson, John Shawe-Taylor, Janez Žerovnik
        Pages 386-398
    9. Online Learning

      1. Chris Mesterharm
        Pages 399-413
      2. Alexey Chernov, Marcus Hutter
        Pages 414-428
    10. Defensive Forecasting

      1. Vladimir Vovk
        Pages 429-443
      2. Vladimir Vovk
        Pages 444-458
      3. Vladimir Vovk, Ilia Nouretdinov, Akimichi Takemura, Glenn Shafer
        Pages 459-473
    11. Teaching

      1. Frank J. Balbach, Thomas Zeugmann
        Pages 474-489
  5. Back Matter

About these proceedings

Keywords

Support Vector Machine algorithmic learning algorithms artificial intelligence automata autonom classification complexity computational learning forecasting inductive inference kernel-based learning machine learning online learning query learning

Editors and affiliations

  • Sanjay Jain
    • 1
  • Hans Ulrich Simon
    • 2
  • Etsuji Tomita
    • 3
  1. 1.School of ComputingNational University of SingaporeSingapore
  2. 2.Ruhr-Universität BochumGermany
  3. 3.Department of Information and Communication Engineering, Faculty of Electro-CommunicationsThe University of Electro-CommunicationsTokyoJapan

Bibliographic information

  • DOI https://doi.org/10.1007/11564089
  • Copyright Information Springer-Verlag Berlin Heidelberg 2005
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-29242-5
  • Online ISBN 978-3-540-31696-1
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site