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Computational Learning Theory

14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001, Amsterdam, The Netherlands, July 16-19, 2001, Proceedings

  • Conference proceedings
  • © 2001

Overview

Editors:
  1. David Helmbold

    1. School of Engineering, Department of Computer Science, University of California, Santa Cruz, Santa Cruz, USA

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  2. Bob Williamson

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

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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 2111)

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

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Table of contents (41 papers)

  1. Front Matter

    Pages I-IX
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  2. How Many Queries Are Needed to Learn One Bit of Information?

    • Hans Ulrich Simon
    Pages 1-13

  3. Radial Basis Function Neural Networks Have Superlinear VC Dimension

    • Michael Schmitt
    Pages 14-30

  4. Tracking a Small Set of Experts by Mixing Past Posteriors

    • Olivier Bousquet, Manfred K. Warmuth
    Pages 31-47

  5. Potential-Based Algorithms in Online Prediction and Game Theory

    • Nicolò Cesa-Bianchi, Gábor Lugosi
    Pages 48-64

  6. A Sequential Approximation Bound for Some Sample-Dependent Convex Optimization Problems with Applications in Learning

    • Tong Zhang
    Pages 65-81

  7. Efficiently Approximating Weighted Sums with Exponentially Many Terms

    • Deepak Chawla, Lin Li, Stephen Scott
    Pages 82-98

  8. Ultraconservative Online Algorithms for Multiclass Problems

    • Koby Crammer, Yoram Singer
    Pages 99-115

  9. Estimating a Boolean Perceptron from Its Average Satisfying Assignment: A Bound on the Precision Required

    • Paul W. Goldberg
    Pages 116-127

  10. Adaptive Strategies and Regret Minimization in Arbitrarily Varying Markov Environments

    • Shie Mannor, Nahum Shimkin
    Pages 128-142

  11. Robust Learning — Rich and Poor

    • John Case, Sanjay Jain, Frank Stephan, Rolf Wiehagen
    Pages 143-159

  12. On the Synthesis of Strategies Identifying Recursive Functions

    • Sandra Zilles
    Pages 160-176

  13. Intrinsic Complexity of Learning Geometrical Concepts from Positive Data

    • Sanjay Jain, Efim Kinber
    Pages 177-193

  14. Toward a Computational Theory of Data Acquisition and Truthing

    • David G. Stork
    Pages 194-207

  15. Discrete Prediction Games with Arbitrary Feedback and Loss (Extended Abstract)

    • Antonio Piccolboni, Christian Schindelhauer
    Pages 208-223

  16. Rademacher and Gaussian Complexities: Risk Bounds and Structural Results

    • Peter L. Bartlett, Shahar Mendelson
    Pages 224-240

  17. Further Explanation of the Effectiveness of Voting Methods: The Game between Margins and Weights

    • Vladimir Koltchinskii, Dmitriy Panchenko, Fernando Lozano
    Pages 241-255

  18. Geometric Methods in the Analysis of Glivenko-Cantelli Classes

    • Shahar Mendelson
    Pages 256-272

  19. Learning Relatively Small Classes

    • Shahar Mendelson
    Pages 273-288

  20. On Agnostic Learning with {0, *, 1}-Valued and Real-Valued Hypotheses

    • Philip M. Long
    Pages 289-302
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Keywords

Editors and Affiliations

  • School of Engineering, Department of Computer Science, University of California, Santa Cruz, Santa Cruz, USA

    David Helmbold

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

    Bob Williamson

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