Machine Learning: ECML-98

1. Front Matter

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2. Learning in agent-oriented worlds

   • Kenneth De Jong

   Pages 1-3

3. Naive (Bayes) at forty: The independence assumption in information retrieval

   • David D. Lewis

   Pages 4-15

4. Learning verbal transitivity using loglinear models

   • Nuno Miguel Marques, Gabriel Pereira Lopes, Carlos Agra Coelho

   Pages 17-24

5. Part-of-speech tagging using decision trees

   • Lluís Màrquez, Horacio Rodríguez

   Pages 25-36

6. Inference of finite automata: Reducing the search space with an ordering of pairs of states

   • François Coste, Jacques Nicolas

   Pages 37-42

7. Automatic acquisition of lexical knowledge from sparse and noisy data

   • René Schneider

   Pages 43-48

8. A normalization method for contextual data: Experience from a large-scale application

   • Sylvain Létourneaue, Stan Matwin, Fazel Familie

   Pages 49-54

9. Learning to classify x-ray images using relational learning

   • Claude Sammutl, Tatjana Zrimec

   Pages 55-60

10. ILP experiments in detecting traffic problems

    • Sašo Džeroskil, Nico Jacobs, Martin Molina, Carlos Moure

    Pages 61-66

11. Simulating children learning and explaining elementary heat transfer phenomena: A multistrategy system at work

    • Filippo Neri

    Pages 67-76

12. Bayes optimal instance-based learning

    • Petri Kontkanen, Petri Myllymdki, Tomi Silander, Henry Tirri

    Pages 77-88

13. Bayesian and information-theoretic priors for Bayesian network parameters

    • Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri, Peter Grünwalde

    Pages 89-94

14. Feature subset selection in text-learning

    • Dunja Mladenić

    Pages 95-100

15. A monotonic measure for optimal feature selection

    • Huan Liul, Hiroshi Motoda, Manoranjan Dash

    Pages 101-106

16. Inducing models of human control skills

    • Rui Camachol

    Pages 107-118

17. God doesn't always shave with Occam's razor — Learning when and how to prune

    • Hilan Bensusan

    Pages 119-124

18. Error estimators for pruning regression trees

    • Luís Torgo

    Pages 125-130

19. Pruning decision trees with misclassification costs

    • Jeffrey P. Bradford, Clayton Kunz, Ron Kohavi, Cliff Brunk, Carla E. Brodley

    Pages 131-136

20. Text categorization with Support Vector Machines: Learning with many relevant features

    • Thorsten Joachims

    Pages 137-142

This book constitutes the refereed proceedings of the 10th European Conference on Machine Learning, ECML-98, held in Chemnitz, Germany, in April 1998.
The book presents 21 revised full papers and 25 short papers reporting on work in progress together with two invited contributions; the papers were selected from a total of 100 submissions. The book is divided in sections on applications of ML, Bayesian networks, feature selection, decision trees, support vector learning, multiple models for classification, inductive logic programming, relational learning, instance-based learning, clustering, genetic algorithms, reinforcement learning and neural networks.

• Bayesian network
• Boosting
• Support Vector Machine
• Text
• algorithms
• machine learning
• programming
• reinforcement learning
• algorithm analysis and problem complexity

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