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Learning Theory

17th Annual Conference on Learning Theory, COLT 2004, Banff, Canada, July 1-4, 2004. Proceedings

  • John Shawe-Taylor
  • Yoram Singer
Conference proceedings COLT 2004

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3120)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 3120)

Table of contents

  1. Front Matter
  2. Economics and Game Theory

    1. Sham M. Kakade, Michael Kearns, Luis E. Ortiz
      Pages 17-32
    2. Sham M. Kakade, Dean P. Foster
      Pages 33-48
  3. OnLine Learning

    1. Nicolò Cesa-Bianchi, Gábor Lugosi, Gilles Stoltz
      Pages 77-92
    2. Nicolò Cesa-Bianchi, Alex Conconi, Claudio Gentile
      Pages 93-108
  4. Inductive Inference

    1. François Denis, Yann Esposito
      Pages 124-139
  5. Probabilistic Models

    1. Evgeny Drukh, Yishay Mansour
      Pages 170-185
    2. Tuğkan Batu, Sudipto Guha, Sampath Kannan
      Pages 186-199
  6. Boolean Function Learning

    1. Dmitry Gavinsky, Avi Owshanko
      Pages 200-209
    2. Dana Angluin, Jiang Chen
      Pages 210-223
    3. Adam R. Klivans, Rocco A. Servedio
      Pages 224-238
  7. Empirical Processes

    1. H. Quang Minh, Thomas Hofmann
      Pages 239-254
    2. Charles A. Micchelli, Massimiliano Pontil
      Pages 255-269
    3. Peter L. Bartlett, Shahar Mendelson, Petra Philips
      Pages 270-284
  8. MDL

  9. Generalisation I

    1. Adam R. Klivans, Rocco A. Servedio
      Pages 348-362
    2. Niko List, Hans Ulrich Simon
      Pages 363-377
  10. Generalisation II

    1. Gilles Blanchard, Christin Schäfer, Yves Rozenholc
      Pages 378-392
    2. Peter Auer, Ronald Ortner
      Pages 408-414
  11. Clustering and Distributed Learning

  12. Boosting

    1. Miroslav Dudík, Steven J. Phillips, Robert E. Schapire
      Pages 472-486
    2. Adam Kalai
      Pages 487-501
    3. Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies
      Pages 502-517
  13. Kernels and Probabilities

    1. Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon
      Pages 518-533
    2. Ran Gilad-Bachrach, Amir Navot, Naftali Tishby
      Pages 549-563
  14. Kernels and Kernel Matrices

    1. David C. Hoyle, Magnus Rattray
      Pages 579-593
    2. Laurent Zwald, Olivier Bousquet, Gilles Blanchard
      Pages 594-608
    3. Mikhail Belkin, Irina Matveeva, Partha Niyogi
      Pages 624-638
  15. Open Problems

    1. Adam R. Klivans, Rocco A. Servedio
      Pages 639-640
    2. Manfred K. Warmuth
      Pages 641-642
    3. Omid Madani, Daniel J. Lizotte, Russell Greiner
      Pages 643-645
  16. Back Matter

About these proceedings

Keywords

Boolean function Boosting algorithmic learning bayesian networks computational learning decision theory game theory inductive inference kernel methods learning learning theory machine learning online learning statistical learning support vector machine

Editors and affiliations

  • John Shawe-Taylor
    • 1
  • Yoram Singer
    • 2
  1. 1.The Centre for Computational Statistics and Machine Learning Department of Computer ScienceUniversity College LondonLondon
  2. 2.GoogleMountain ViewUSA

Bibliographic information

  • DOI https://doi.org/10.1007/b98522
  • Copyright Information Springer-Verlag Berlin Heidelberg 2004
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-22282-8
  • Online ISBN 978-3-540-27819-1
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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