Skip to main content
Springer Nature Link
Log in

Machine Learning: ECML-94

European Conference on Machine Learning, Catania, Italy, April 6-8, 1994. Proceedings

  • Conference proceedings
  • © 1994

Overview

Editors:
  1. Francesco Bergadano
    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Luc Raedt
    View editor publications

    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 784)

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

Included in the following conference series:

Conference proceedings info: ECML 1994.

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

About this book

This volume contains the proceedings of the European Conference on Machine Learning 1994, which continues the tradition of earlier meetings and which is a major forum for the presentation of the latest and most significant results in machine learning.
Machine learning is one of the most important subfields of artificial intelligence and computer science, as it is concerned with the automation of learning processes.
This volume contains two invited papers, 19 regular papers, and 25 short papers carefully reviewed and selected from in total 88 submissions.
The papers describe techniques, algorithms, implementations, and experiments in the area of machine learning.

Similar content being viewed by others

Keywords

Table of contents (46 papers)

  1. Front Matter

    Download chapter PDF

  2. Industrial applications of ML: Illustrations for the KAML dilemma and the CBR dream

    • Y. Kodratoff
    Pages 1-19

  3. Knowledge representation in machine learning

    • Filippo Neri, Lorenza Saitta
    Pages 20-27

  4. Inverting implication with small training sets

    • David W. Aha, Stephane Lapointe, Charles X. Ling, Stan Matwin
    Pages 29-48

  5. A context similarity measure

    • Yoram Biberman
    Pages 49-63

  6. Incremental learning of control knowledge for nonlinear problem solving

    • Daniel Borrajo, Manuela Veloso
    Pages 64-82

  7. Characterizing the applicability of classification algorithms using meta-level learning

    • Pavel Brazdil, Joāo Gama, Bob Henery
    Pages 83-102

  8. Inductive learning of characteristic concept descriptions from small sets of classified examples

    • Werner Emde
    Pages 103-121

  9. FOSSIL: A robust relational learner

    • Johannes Fürnkranz
    Pages 122-137

  10. A multistrategy learning system and its integration into an interactive floorplanning tool

    • Jürgen Herrmann, Reiner Ackermann, Jörg Peters, Detlef Reipa
    Pages 138-153

  11. Bottom-up induction of oblivious read-once decision graphs

    • Ron Kohavi
    Pages 154-169

  12. Estimating attributes: Analysis and extensions of RELIEF

    • Igor Kononenko
    Pages 171-182

  13. BMWk revisited generalization and formalization of an algorithm for detecting recursive relations in term sequences

    • Guillaume Le Blanc
    Pages 183-197

  14. An analytic and empirical comparison of two methods for discovering probabilistic causal relationships

    • Donate Malerba, Giovanni Semeraro, Floriana Esposito
    Pages 198-216

  15. Sample PAC-learnability in model inference

    • S. H. Nienhuys-Cheng, M. Polman
    Pages 217-230

  16. Averaging over decision stumps

    • Jonathan J. Oliver, David Hand
    Pages 231-241

  17. Controlling constructive induction in CIPF: An MDL approach

    • Bernhard Pfahringer
    Pages 242-256

  18. Using constraints to building version spaces

    • Michèle Sebag
    Pages 257-271

  19. On the utility of predicate invention in inductive logic programming

    • Irene Stahl
    Pages 272-286

  20. Learning problem-solving concepts by reflecting on problem solving

    • Eleni Stroulia, Ashok K. Goel
    Pages 287-306
Back to top

Bibliographic Information

  • Book Title: Machine Learning: ECML-94

  • Book Subtitle: European Conference on Machine Learning, Catania, Italy, April 6-8, 1994. Proceedings

  • Editors: Francesco Bergadano, Luc Raedt

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/3-540-57868-4

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Berlin Heidelberg 1994

  • Softcover ISBN: 978-3-540-57868-0Published: 22 March 1994

  • eBook ISBN: 978-3-540-48365-6Published: 07 July 2005

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XIII, 447

  • Topics: Artificial Intelligence

Publish with us

Policies and ethics

Back to top