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  • Conference proceedings
  • © 2006

Learning Theory

19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 4005)

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 2006.

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Table of contents (48 papers)

  1. Front Matter

  2. Invited Presentations

    1. Random Multivariate Search Trees

      • Luc Devroye
      Pages 1-1
    2. On Learning and Logic

      • György Turán
      Pages 2-3
    3. Predictions as Statements and Decisions

      • Vladimir Vovk
      Pages 4-4
  3. Clustering, Un-, and Semisupervised Learning

    1. A Sober Look at Clustering Stability

      • Shai Ben-David, Ulrike von Luxburg, Dávid Pál
      Pages 5-19
    2. PAC Learning Axis-Aligned Mixtures of Gaussians with No Separation Assumption

      • Jon Feldman, Rocco A. Servedio, Ryan O’Donnell
      Pages 20-34
    3. Stable Transductive Learning

      • Ran El-Yaniv, Dmitry Pechyony
      Pages 35-49
  4. Statistical Learning Theory

    1. Function Classes That Approximate the Bayes Risk

      • Ingo Steinwart, Don Hush, Clint Scovel
      Pages 79-93
    2. Functional Classification with Margin Conditions

      • Magalie Fromont, Christine Tuleau
      Pages 94-108
  5. Regularized Learning and Kernel Methods

    1. Maximum Entropy Distribution Estimation with Generalized Regularization

      • Miroslav Dudík, Robert E. Schapire
      Pages 123-138
    2. Mercer’s Theorem, Feature Maps, and Smoothing

      • Ha Quang Minh, Partha Niyogi, Yuan Yao
      Pages 154-168
    3. Learning Bounds for Support Vector Machines with Learned Kernels

      • Nathan Srebro, Shai Ben-David
      Pages 169-183
  6. Query Learning and Teaching

    1. On Optimal Learning Algorithms for Multiplicity Automata

      • Laurence Bisht, Nader H. Bshouty, Hanna Mazzawi
      Pages 184-198
    2. Exact Learning Composed Classes with a Small Number of Mistakes

      • Nader H. Bshouty, Hanna Mazzawi
      Pages 199-213
    3. DNF Are Teachable in the Average Case

      • Homin K. Lee, Rocco A. Servedio, Andrew Wan
      Pages 214-228
    4. Teaching Randomized Learners

      • Frank J. Balbach, Thomas Zeugmann
      Pages 229-243

Other Volumes

  1. Learning Theory

Editors and Affiliations

  • ICREA and Department of Economics, Universitat Pompeu Fabra, Barcelona, Spain

    Gábor Lugosi

  • Ruhr-Universität Bochum, Germany

    Hans Ulrich Simon

Bibliographic Information

Buy it now

Buying options

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