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  • Conference proceedings
  • © 2004

Learning Theory

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

Editors:

  1. John Shawe-Taylor

    1. The Centre for Computational Statistics and Machine Learning Department of Computer Science, University College London, London

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  2. Yoram Singer

    1. Google, Mountain View, USA

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    You can also search for this editor in PubMed   Google Scholar

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

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Conference series link(s): COLT: International Conference on Computational Learning Theory

Conference proceedings info: COLT 2004.

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Table of contents (46 papers)

  1. Front Matter

    PDF

  2. Economics and Game Theory

    2. Graphical Economics

      • Sham M. Kakade, Michael Kearns, Luis E. Ortiz
      Pages 17-32

    3. Deterministic Calibration and Nash Equilibrium

      • Sham M. Kakade, Dean P. Foster
      Pages 33-48

  3. OnLine Learning

    2. Minimizing Regret with Label Efficient Prediction

      • Nicolò Cesa-Bianchi, Gábor Lugosi, Gilles Stoltz
      Pages 77-92

    3. Regret Bounds for Hierarchical Classification with Linear-Threshold Functions

      • Nicolò Cesa-Bianchi, Alex Conconi, Claudio Gentile
      Pages 93-108

  4. Inductive Inference

    1. Learning Classes of Probabilistic Automata

      • François Denis, Yann Esposito
      Pages 124-139

  5. Probabilistic Models

    1. Concentration Bounds for Unigrams Language Model

      • Evgeny Drukh, Yishay Mansour
      Pages 170-185

    2. Inferring Mixtures of Markov Chains

      • Tuğkan Batu, Sudipto Guha, Sampath Kannan
      Pages 186-199

  6. Boolean Function Learning

    1. PExact = Exact Learning

      • Dmitry Gavinsky, Avi Owshanko
      Pages 200-209

    2. Learning a Hidden Graph Using O(log n) Queries Per Edge

      • Dana Angluin, Jiang Chen
      Pages 210-223

    3. Toward Attribute Efficient Learning of Decision Lists and Parities

      • Adam R. Klivans, Rocco A. Servedio
      Pages 224-238

  7. Empirical Processes

    1. Learning Over Compact Metric Spaces

      • H. Quang Minh, Thomas Hofmann
      Pages 239-254

    2. A Function Representation for Learning in Banach Spaces

      • Charles A. Micchelli, Massimiliano Pontil
      Pages 255-269

    3. Local Complexities for Empirical Risk Minimization

      • Peter L. Bartlett, Shahar Mendelson, Petra Philips
      Pages 270-284
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Editors and Affiliations

  • The Centre for Computational Statistics and Machine Learning Department of Computer Science, University College London, London

    John Shawe-Taylor

  • Google, Mountain View, USA

    Yoram Singer

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Bibliographic Information

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Buy it now

eBook USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

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