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  • Conference proceedings
  • © 2003

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

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

  2. Target Area: Computational Game Theory

    1. Invited Talk

    2. 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
    3. 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
  3. Poster Session 1

    1. Multiplicative Updates for Large Margin Classifiers

      • Fei Sha, Lawrence K. Saul, Daniel D. Lee
      Pages 188-202
    2. Simplified PAC-Bayesian Margin Bounds

      • David McAllester
      Pages 203-215
    3. Sparse Kernel Partial Least Squares Regression

      • Michinari Momma, Kristin P. Bennett
      Pages 216-230
    4. Sparse Probability Regression by Label Partitioning

      • Shantanu Chakrabartty, Gert Cauwenberghs, Jayadeva
      Pages 231-242
    5. Learning with Rigorous Support Vector Machines

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

Keywords

  • Algorithmic Learning
  • Boosting
  • Game Theory
  • Inductive Inference
  • Kernel Methods
  • Learning Classifier Systems
  • Machine Learning
  • Online Learning
  • Statistical Learning
  • Support Vector Machine
  • algorithm
  • algorithms
  • complexity
  • k-Means
  • programming
  • algorithm analysis and problem complexity

Editors and Affiliations

  • MPI for Biological Cybernetics, Tübingen, Germany

    Bernhard Schölkopf

  • University of California, Santa Cruz

    Manfred K. Warmuth

Bibliographic Information

  • Book Title: Learning Theory and Kernel Machines

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

  • Editors: Bernhard Schölkopf, Manfred K. Warmuth

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/b12006

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2003

  • Softcover ISBN: 978-3-540-40720-1Published: 11 August 2003

  • eBook ISBN: 978-3-540-45167-9Published: 11 November 2003

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XIV, 754

  • Topics: Artificial Intelligence, Theory of Computation, Algorithms, Formal Languages and Automata Theory

Buying options

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions