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Learning Theory

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

  • Conference proceedings
  • © 2004

Overview

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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    You can also search for this editor in PubMed   Google Scholar

  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)

Included in the following conference series:

Conference proceedings info: COLT 2004.

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Table of contents (46 papers)

  1. Empirical Processes

    1. Model Selection by Bootstrap Penalization for Classification

      • Magalie Fromont
      Pages 285-299

  2. MDL

    1. Convergence of Discrete MDL for Sequential Prediction

      • Jan Poland, Marcus Hutter
      Pages 300-314

    2. On the Convergence of MDL Density Estimation

      • Tong Zhang
      Pages 315-330

    3. Suboptimal Behavior of Bayes and MDL in Classification Under Misspecification

      • Peter Grünwald, John Langford
      Pages 331-347

  3. Generalisation I

    1. Learning Intersections of Halfspaces with a Margin

      • Adam R. Klivans, Rocco A. Servedio
      Pages 348-362

    2. A General Convergence Theorem for the Decomposition Method

      • Niko List, Hans Ulrich Simon
      Pages 363-377

  4. Generalisation II

    1. Oracle Bounds and Exact Algorithm for Dyadic Classification Trees

      • Gilles Blanchard, Christin Schäfer, Yves Rozenholc
      Pages 378-392

    2. An Improved VC Dimension Bound for Sparse Polynomials

      • Michael Schmitt
      Pages 393-407

    3. A New PAC Bound for Intersection-Closed Concept Classes

      • Peter Auer, Ronald Ortner
      Pages 408-414

  5. Clustering and Distributed Learning

    1. A Framework for Statistical Clustering with a Constant Time Approximation Algorithms for K-Median Clustering

      • Shai Ben-David
      Pages 415-426

    2. Data Dependent Risk Bounds for Hierarchical Mixture of Experts Classifiers

      • Arik Azran, Ron Meir
      Pages 427-441

    3. Consistency in Models for Communication Constrained Distributed Learning

      • J. B. Predd, S. R. Kulkarni, H. V. Poor
      Pages 442-456

    4. On the Convergence of Spectral Clustering on Random Samples: The Normalized Case

      • Ulrike von Luxburg, Olivier Bousquet, Mikhail Belkin
      Pages 457-471

  6. Boosting

    1. Performance Guarantees for Regularized Maximum Entropy Density Estimation

      • Miroslav Dudík, Steven J. Phillips, Robert E. Schapire
      Pages 472-486

    2. Learning Monotonic Linear Functions

      • Adam Kalai
      Pages 487-501

    3. Boosting Based on a Smooth Margin

      • Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies
      Pages 502-517

  7. Kernels and Probabilities

    1. Bayesian Networks and Inner Product Spaces

      • Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon
      Pages 518-533

    2. An Inequality for Nearly Log-Concave Distributions with Applications to Learning

      • Constantine Caramanis, Shie Mannor
      Pages 534-548

    3. Bayes and Tukey Meet at the Center Point

      • Ran Gilad-Bachrach, Amir Navot, Naftali Tishby
      Pages 549-563

    4. Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results

      • Peter L. Bartlett, Ambuj Tewari
      Pages 564-578
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  1. Learning Theory

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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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