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Table of contents

  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
  21. Characterizing rational versus exponential learning curves
    Dale Schuurmans
    Pages 272-286
  22. Is pocket algorithm optimal?
    Marco Muselli
    Pages 287-297
  23. Some theorems concerning the free energy of (Un) constrained stochastic Hopfield neural networks
    Jan van den Berg, Jan C. Bioch
    Pages 298-312
  24. A space-bounded learning algorithm for axis-parallel rectangles
    Foued Ameur
    Pages 313-321
  25. Learning decision lists and trees with equivalence-queries
    Hans Ulrich Simon
    Pages 322-336
  26. Bounding VC-dimension for neural networks: Progress and prospects
    Marek Karpinski, Angus Macintyre
    Pages 337-341
  27. Average case analysis of a learning algorithm for μ-DNF expressions
    Mostefa Golea
    Pages 342-356
  28. Learning by extended statistical queries and its relation to PAC learning
    Eli Shamir, Clara Shwartzman
    Pages 357-366
  29. Typed pattern languages and their learnability
    Takeshi Koshiba
    Pages 367-379
  30. Learning behaviors of automata from shortest counterexamples
    F. Bergadano, S. Varricchio
    Pages 380-391
  31. Learning of regular expressions by pattern matching
    Alvis Brāzma
    Pages 392-403
  32. The query complexity of learning some subclasses of context-free grammars
    Carlos Domingo, Víctor Lavín
    Pages 404-414
  33. Back Matter
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About these proceedings

Introduction

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.

Keywords

Algorithms algorithm evolutionary algorithm learning learning theory logic machine learning programming robot robotics

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-59119-2
  • Copyright Information Springer-Verlag Berlin Heidelberg 1995
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-59119-1
  • Online ISBN 978-3-540-49195-8
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site