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Learning Theory and Kernel Machines

16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003, Proceedings

  • Conference proceedings
  • © 2003

Overview

Editors:
  1. Bernhard Schölkopf

    1. MPI for Biological Cybernetics, Tübingen, Germany

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    You can also search for this editor in PubMed   Google Scholar

  2. Manfred K. Warmuth

    1. University of California, Santa Cruz

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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 2777)

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

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Table of contents (60 papers)

  1. Front Matter

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  2. Target Area: Computational Game Theory

    1. Tutorial: Learning Topics in Game-Theoretic Decision Making

      • Michael L. Littman
      Pages 1-1

    2. Invited Talk

      1. A General Class of No-Regret Learning Algorithms and Game-Theoretic Equilibria
        • Amy Greenwald, Amir Jafari
        Pages 2-12

    3. Contributed Talks

      1. Preference Elicitation and Query Learning
        • Avrim Blum, Jeffrey C. Jackson, Tuomas Sandholm, Martin Zinkevich
        Pages 13-25
      2. Efficient Algorithms for Online Decision Problems
        • Adam Kalai, Santosh Vempala
        Pages 26-40

    4. Kernel Machines

      1. Positive Definite Rational Kernels
        • Corinna Cortes, Patrick Haffner, Mehryar Mohri
        Pages 41-56
      2. Bhattacharyya and Expected Likelihood Kernels
        • Tony Jebara, Risi Kondor
        Pages 57-71
      3. Maximal Margin Classification for Metric Spaces
        • Matthias Hein, Olivier Bousquet
        Pages 72-86
      4. Maximum Margin Algorithms with Boolean Kernels
        • Roni Khardon, Rocco A. Servedio
        Pages 87-101
      5. Knowledge-Based Nonlinear Kernel Classifiers
        • Glenn M. Fung, Olvi L. Mangasarian, Jude W. Shavlik
        Pages 102-113
      6. Fast Kernels for Inexact String Matching
        • Christina Leslie, Rui Kuang
        Pages 114-128
      7. On Graph Kernels: Hardness Results and Efficient Alternatives
        • Thomas Gärtner, Peter Flach, Stefan Wrobel
        Pages 129-143
      8. Kernels and Regularization on Graphs
        • Alexander J. Smola, Risi Kondor
        Pages 144-158
      9. Data-Dependent Bounds for Multi-category Classification Based on Convex Losses
        • Ilya Desyatnikov, Ron Meir
        Pages 159-172

  3. Poster Session 1

    1. Comparing Clusterings by the Variation of Information

      • Marina Meilă
      Pages 173-187

    2. Multiplicative Updates for Large Margin Classifiers

      • Fei Sha, Lawrence K. Saul, Daniel D. Lee
      Pages 188-202

    3. Simplified PAC-Bayesian Margin Bounds

      • David McAllester
      Pages 203-215

    4. Sparse Kernel Partial Least Squares Regression

      • Michinari Momma, Kristin P. Bennett
      Pages 216-230

    5. Sparse Probability Regression by Label Partitioning

      • Shantanu Chakrabartty, Gert Cauwenberghs, Jayadeva
      Pages 231-242

    6. Learning with Rigorous Support Vector Machines

      • Jinbo Bi, Vladimir N. Vapnik
      Pages 243-257
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Keywords

Editors and Affiliations

  • MPI for Biological Cybernetics, Tübingen, Germany

    Bernhard Schölkopf

  • University of California, Santa Cruz

    Manfred K. Warmuth

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