Computational Learning Theory

14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001 Amsterdam, The Netherlands, July 16–19, 2001 Proceedings

  • David Helmbold
  • Bob Williamson

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2111)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 2111)

Table of contents

  1. Front Matter
    Pages I-IX
  2. Olivier Bousquet, Manfred K. Warmuth
    Pages 31-47
  3. Nicolò Cesa-Bianchi, Gábor Lugosi
    Pages 48-64
  4. Deepak Chawla, Lin Li, Stephen Scott
    Pages 82-98
  5. Koby Crammer, Yoram Singer
    Pages 99-115
  6. John Case, Sanjay Jain, Frank Stephan, Rolf Wiehagen
    Pages 143-159
  7. Antonio Piccolboni, Christian Schindelhauer
    Pages 208-223
  8. Peter L. Bartlett, Shahar Mendelson
    Pages 224-240
  9. Vladimir Koltchinskii, Dmitriy Panchenko, Fernando Lozano
    Pages 241-255
  10. Shahar Mendelson
    Pages 273-288

About these proceedings

Keywords

Algorithmic Learning Boosting Classification Computational Learning Computational Learning Theory Data Mining Game Theory Inference Q-Learning algorithms cognition complexity kernel method learning theory optimization

Editors and affiliations

  • David Helmbold
    • 1
  • Bob Williamson
    • 2
  1. 1.School of Engineering, Department of Computer ScienceUniversity of California, Santa CruzSanta CruzUSA
  2. 2.Research School of Information Sciences and Engineering Department of Telecommunications EngineeringAustralian National UniversityCanberraAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-44581-1
  • Copyright Information Springer-Verlag Berlin Heidelberg 2001
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-42343-0
  • Online ISBN 978-3-540-44581-4
  • Series Print ISSN 0302-9743
  • About this book