Machine Learning: ECML-93

1. Front Matter

   PDF

2. FOIL: A midterm report

   • J. R. Quinlan, R. M. Cameron-Jones

   Pages 1-20

3. Inductive logic programming: Derivations, successes and shortcomings

   • Stephen Muggleton

   Pages 21-37

4. Two methods for improving inductive logic programming systems

   • Irene Stahl, Birgit Tausend, Rüdiger Wirth

   Pages 39-55

5. Generalization under implication by using or-introduction

   • Peter Idestam-Almquist

   Pages 56-64

6. On the proper definition of minimality in specialization and theory revision

   • Stefan Wrobel

   Pages 65-82

7. Predicate invention in inductive data engineering

   • Peter A. Flach

   Pages 83-94

8. Subsumption and refinement in model inference

   • Patrick R. J. van der Laag, Shan-Hwei Nienhuys-Cheng

   Pages 95-114

9. Some lower bounds for the computational complexity of inductive logic programming

   • Jörg-Uwe Kietz

   Pages 115-123

10. Improving example-guided unfolding

    • Henrik Boström

    Pages 124-135

11. Bayes and pseudo-Bayes estimates of conditional probabilities and their reliability

    • James Cussens

    Pages 136-152

12. Induction of recursive Bayesian classifiers

    • Pat Langley

    Pages 153-164

13. Decision tree pruning as a search in the state space

    • Floriana Esposito, Donate Malerba, Giovanni Semeraro

    Pages 165-184

14. Controlled redundancy in incremental rule learning

    • Luis Torgo

    Pages 185-195

15. Getting order independence in incremental learning

    • Antoine Cornuéjols

    Pages 196-212

16. Feature selection using rough sets theory

    • Maciej Modrzejewski

    Pages 213-226

17. Effective learning in dynamic environments by explicit context tracking

    • Gerhard Widmer, Miroslav Kubat

    Pages 227-243

18. COBBIT—A control procedure for COBWEB in the presence of concept drift

    • Fredrik Kilander, Carl Gustaf Jansson

    Pages 244-261

19. Genetic algorithms for protein tertiary structure prediction

    • Steffen Schulze-Kremer

    Pages 262-279

20. SIA: A supervised inductive algorithm with genetic search for learning attributes based concepts

    • Gilles Venturini

    Pages 280-296

This volume contains the proceedings of the Eurpoean Conference on Machine Learning (ECML-93), continuing the tradition of the five earlier EWSLs (European Working Sessions on Learning). The aim of these conferences is to provide a platform for presenting the latest results in the area of machine learning. The ECML-93 programme included invited talks, selected papers, and the presentation of ongoing work in poster sessions. The programme was completed by several workshops on specific topics. The volume contains papers related to all these activities. The first chapter of the proceedings contains two invited papers, one by Ross Quinlan and one by Stephen Muggleton on inductive logic programming. The second chapter contains 18 scientific papers accepted for the main sessions of the conference. The third chapter contains 18 shorter position papers. The final chapter includesthree overview papers related to the ECML-93 workshops.

• Genetic Algorithms
• Genetische Algorithmen
• Inductive Learning
• Inductive Loigic
• Induktives Lernen
• Induktives Logisches Programmieren
• Learnability
• Lernbarkeit
• complexity
• complexity theory
• heuristics
• learning
• machine learning
• programming
• proving

Policies and ethics