Machine Learning: ECML'97: 9th European Conference on Machine Learning, Prague, Czech Republic, April 23 - 25, 1997, Proceedings | SpringerLink
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Machine Learning: ECML'97

9th European Conference on Machine Learning, Prague, Czech Republic, April 23 - 25, 1997, Proceedings

  • Conference proceedings
  • © 1997

Overview

Editors:
  1. Maarten Someren
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  2. Gerhard Widmer
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 1224)

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

Included in the following conference series:

Conference proceedings info: ECML 1997.

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About this book

This book constitutes the refereed proceedings of the Ninth European Conference on Machine Learning, ECML-97, held in Prague, Czech Republic, in April 1997.
This volume presents 26 revised full papers selected from a total of 73 submissions. Also included are an abstract and two papers corresponding to the invited talks as well as descriptions from four satellite workshops. The volume covers the whole spectrum of current machine learning issues.

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Table of contents (33 papers)

  1. Front Matter

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  2. Uncertain learning agents

    • Stuart Russell
    Pages 1-3

  3. Constructing and sharing perceptual distinctions

    • Luc Steels
    Pages 4-13

  4. On prediction by data compression

    • Paul Vitányi, Ming Li
    Pages 14-30

  5. Induction of feature terms with INDIE

    • Eva Armengol, Enric Plaza
    Pages 31-48

  6. Exploiting qualitative knowledge to enhance skill acquisition

    • Cristina Baroglio
    Pages 49-56

  7. Integrated learning and planning based on truncating temporal differences

    • Paweł Cichosz
    Pages 57-72

  8. θ-subsumption for structural matching

    • Luc De Raedt, Peter Idestam-Almquist, Gunther Sablon
    Pages 73-84

  9. Classification by Voting Feature Intervals

    • Gülşen Demiröz, H. Altay Güvenir
    Pages 85-92

  10. Constructing intermediate concepts by decomposition of real functions

    • Janez Demšar, Blaž Zupan, Marko Bohanec, Ivan Bratko
    Pages 93-107

  11. Conditions for Occam's razor applicability and noise elimination

    • Dragan Gamberger, Nada Lavrač
    Pages 108-123

  12. Learning different types of new attributes by combining the neural network and iterative attribute construction

    • Yuh-Jyh Hu
    Pages 124-137

  13. Metrics on terms and clauses

    • Alan Hutchinson
    Pages 138-145

  14. Learning when negative examples abound

    • Miroslav Kubat, Robert Holte, Stan Matwin
    Pages 146-153

  15. A model for generalization based on confirmatory induction

    • Nicolas Lachiche, Pierre Marquis
    Pages 154-161

  16. Learning Linear Constraints in Inductive Logic Programming

    • Lionel Martin, Christel Vrain
    Pages 162-169

  17. Finite-Element methods with local triangulation refinement for continuous reinforcement learning problems

    • Rémi Munos
    Pages 170-182

  18. Inductive Genetic Programming with Decision Trees

    • Nikolay I. Nikolaev, Vanio Slavov
    Pages 183-190

  19. Parallel and distributed search for structure in multivariate time series

    • Tim Oates, Matthew D. Schmill, Paul R. Cohen
    Pages 191-198

  20. Compression-based pruning of decision lists

    • Bernhard Pfahringer
    Pages 199-212
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Bibliographic Information

  • Book Title: Machine Learning: ECML'97

  • Book Subtitle: 9th European Conference on Machine Learning, Prague, Czech Republic, April 23 - 25, 1997, Proceedings

  • Editors: Maarten Someren, Gerhard Widmer

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/3-540-62858-4

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Berlin Heidelberg 1997

  • Softcover ISBN: 978-3-540-62858-3Published: 09 April 1997

  • eBook ISBN: 978-3-540-68708-5Published: 07 July 2005

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XIV, 366

  • Topics: Artificial Intelligence, Algorithm Analysis and Problem Complexity

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