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Computational Learning Theory

15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002. Proceedings

  • Conference proceedings
  • © 2002

Overview

Editors:
  1. Jyrki Kivinen

    1. Research School of Information Sciences and Engineering, Australian National University, Canberra, Australia

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    You can also search for this editor in PubMed   Google Scholar

  2. Robert H. Sloan

    1. Computer Science Department, University of Illinois at Chicago, Chicago, 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 2375)

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

Included in the following conference series:

Conference proceedings info: COLT 2002.

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Table of contents (27 papers)

  1. Front Matter

    Pages I-XI
    Download chapter PDF

  2. Statistical Learning Theory

    1. Agnostic Learning Nonconvex Function Classes

      • Shahar Mendelson, Robert C. Williamson
      Pages 1-13

    2. Entropy, Combinatorial Dimensions and Random Averages

      • Shahar Mendelson, Roman Vershynin
      Pages 14-28

    3. Geometric Parameters of Kernel Machines

      • Shahar Mendelson
      Pages 29-43

    4. Localized Rademacher Complexities

      • Peter L. Bartlett, Olivier Bousquet, Shahar Mendelson
      Pages 44-58

    5. Some Local Measures of Complexity of Convex Hulls and Generalization Bounds

      • Olivier Bousquet, Vladimir Koltchinskii, Dmitriy Panchenko
      Pages 59-73

  3. Online Learning

    1. Path Kernels and Multiplicative Updates

      • Eiji Takimoto, Manfred K. Warmuth
      Pages 74-89

    2. Predictive Complexity and Information

      • Michael V. Vyugin, Vladimir V. V’yugin
      Pages 90-105

    3. Mixability and the Existence of Weak Complexities

      • Yuri Kalnishkan, Michael V. Vyugin
      Pages 105-120

    4. A Second-Order Perceptron Algorithm

      • Nicolò Cesa-Bianchi, Alex Conconi, Claudio Gentile
      Pages 121-137

    5. Tracking Linear-Threshold Concepts with Winnow

      • Chris Mesterharm
      Pages 138-153

  4. Inductive Inference

    1. Learning Tree Languages from Text

      • Henning Fernau
      Pages 153-168

    2. Polynomial Time Inductive Inference of Ordered Tree Patterns with Internal Structured Variables from Positive Data

      • Yusuke Suzuki, Ryuta Akanuma, Takayoshi Shoudai, Tetsuhiro Miyahara, Tomoyuki Uchida
      Pages 169-184

    3. Inferring Deterministic Linear Languages

      • Colin de la Higuera, Jose Oncina
      Pages 185-200

    4. Merging Uniform Inductive Learners

      • Sandra Zilles
      Pages 201-216

    5. The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions

      • Jürgen Schmidhuber
      Pages 216-228

  5. PAC Learning

    1. New Lower Bounds for Statistical Query Learning

      • Ke Yang
      Pages 229-243

    2. Exploring Learnability between Exact and PAC

      • Nader H. Bshouty, Jeffrey C. Jackson, Christino Tamon
      Pages 244-254

    3. PAC Bounds for Multi-armed Bandit and Markov Decision Processes

      • Eyal Even-Dar, Shie Mannor, Yishay Mansour
      Pages 255-270

    4. Bounds for the Minimum Disagreement Problem with Applications to Learning Theory

      • Nader H. Bshouty, Lynn Burroughs
      Pages 271-286
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  1. Computational Learning Theory

Keywords

Editors and Affiliations

  • Research School of Information Sciences and Engineering, Australian National University, Canberra, Australia

    Jyrki Kivinen

  • Computer Science Department, University of Illinois at Chicago, Chicago, USA

    Robert H. Sloan

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