Machine Learning: ECML-94

1. Front Matter

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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

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.

• Algorithmic Learning
• Algorithmisches Lernen
• Inductive Learning
• Inductive Logic Programming
• Induktives Lernen
• Induktives logisches Programmieren
• Multi-strategy Learning
• artificial intelligence
• automation
• classification
• complexity
• genetic programming
• knowledge representation
• learning
• machine learning

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