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Computational Learning Theory

Second European Conference, EuroCOLT '95, Barcelona, Spain, March 13 - 15, 1995. Proceedings

  • Conference proceedings
  • © 1995

Overview

Editors:
  1. Paul Vitányi
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 904)

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

Included in the following conference series:

Conference proceedings info: EuroCOLT 1995.

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Table of contents (31 papers)

  1. Front Matter

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  2. The discovery of algorithmic probability: A guide for the programming of true creativity

    • Ray J. Solomonoff
    Pages 1-22

  3. A desicion-theoretic generalization of on-line learning and an application to boosting

    • Yoav Freund, Robert E. Schapire
    Pages 23-37

  4. Online learning versus offline learning

    • Shai Ben-David, Eyal Kushilevitz, Yishay Mansour
    Pages 38-52

  5. Learning distributions by their density levels — A paradigm for learning without a teacher

    • Shai Ben-David, Michael Lindenbaum
    Pages 53-68

  6. Tight worst-case loss bounds for predicting with expert advice

    • David Haussler, Jyrki Kivinen, Manfred K. Warmuth
    Pages 69-83

  7. On-line maximum likelihood prediction with respect to general loss functions

    • Kenji Yamanishi
    Pages 84-98

  8. The power of procrastination in inductive inference: How it depends on used ordinal notations

    • Andris Ambainis
    Pages 99-111

  9. Learnability of Kolmogorov-easy circuit expressions via queries

    • José L. Balcázar, Harry Buhrman, Montserrat Hermo
    Pages 112-124

  10. Trading monotonicity demands versus mind changes

    • Steffen Lange, Thomas Zeugmann
    Pages 125-139

  11. Learning recursive functions from approximations

    • John Case, Susanne Kaufmann, Efim Kinber, Martin Kummer
    Pages 140-153

  12. On the intrinsic complexity of learning

    • Rūsiņš Freivalds, Efim Kinber, Carl H. Smith
    Pages 154-168

  13. The structure of intrinsic complexity of learning

    • Sanjay Jain, Arun Sharma
    Pages 169-181

  14. Kolmogorov numberings and minimal identification

    • Rusins Freivalds, Sanjay Jain
    Pages 182-195

  15. Stochastic complexity in learning

    • J. Rissanen
    Pages 196-210

  16. Function learning from interpolation (extended abstract)

    • Martin Anthony, Peter Bartlett
    Pages 211-221

  17. Approximation and learning of convex superpositions

    • Leonid Gurvits, Pascal Koiran
    Pages 222-236

  18. Minimum description length estimators under the optimal coding scheme

    • V. G. Vovk
    Pages 237-251

  19. MDL learning of unions of simple pattern languages from positive examples

    • Pekka Kilpeläinen, Heikki Mannila, Esko Ukkonen
    Pages 252-260

  20. A note on the use of probabilities by mechanical learners

    • Eric Martin, Daniel Osherson
    Pages 261-271
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About this book

This volume presents the proceedings of the Second European Conference on Computational Learning Theory (EuroCOLT '95), held in Barcelona, Spain in March 1995.
The book contains full versions of the 28 papers accepted for presentation at the conference as well as three invited papers. All relevant topics in fundamental studies of computational aspects of artificial and natural learning systems and machine learning are covered; in particular artificial and biological neural networks, genetic and evolutionary algorithms, robotics, pattern recognition, inductive logic programming, decision theory, Bayesian/MDL estimation, statistical physics, and cryptography are addressed.

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