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Machine Learning: ECML 2002

13th European Conference on Machine Learning, Helsinki, Finland, August 19-23, 2002. Proceedings

  • Conference proceedings
  • © 2002

Overview

Editors:
  1. Tapio Elomaa

    1. Department of Computer Science, University of Helsinki, Helsinki, Finland

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  2. Heikki Mannila

    1. Department of Computer Science, University of Helsinki, Helsinki, Finland

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  3. Hannu Toivonen

    1. Department of Computer Science, University of Helsinki, Helsinki, Finland

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2430)

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

Included in the following conference series:

Conference proceedings info: ECML 2002.

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Table of contents (45 papers)

  1. Front Matter

    Pages I-XIII
    Download chapter PDF

  2. Contributed Papers

    1. Convergent Gradient Ascent in General-Sum Games

      • Bikramjit Banerjee, Jing Peng
      Pages 1-9

    2. Revising Engineering Models: Combining Computational Discovery with Knowledge

      • Stephen D. Bay, Daniel G. Shapiro, Pat Langley
      Pages 10-22

    3. Variational Extensions to EM and Multinomial PCA

      • Wray Buntine
      Pages 23-34

    4. Learning and Inference for Clause Identification

      • Xavier Carreras, Lluís Màrquez, Vasin Punyakanok, Dan Roth
      Pages 35-47

    5. An Empirical Study of Encoding Schemes and Search Strategies in Discovering Causal Networks

      • Honghua Dai, Gang Li, Yiqing Tu
      Pages 48-59

    6. Variance Optimized Bagging

      • Philip Derbeko, Ran El-Yaniv, Ron Meir
      Pages 60-72

    7. How to Make AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the Code

      • Günther Eibl, Karl Peter Pfeiffer
      Pages 72-83

    8. Sparse Online Greedy Support Vector Regression

      • Yaakov Engel, Shie Mannor, Ron Meir
      Pages 84-96

    9. Pairwise Classification as an Ensemble Technique

      • Johannes Fürnkranz
      Pages 97-110

    10. RIONA: A Classifier Combining Rule Induction and k-NN Method with Automated Selection of Optimal Neighbourhood

      • Grzegorz Góra, Arkadiusz Wojna
      Pages 111-123

    11. Using Hard Classifiers to Estimate Conditional Class Probabilities

      • Ole Martin Halck
      Pages 124-134

    12. Evidence that Incremental Delta-Bar-Delta Is an Attribute-Efficient Linear Learner

      • Harlan D. Harris
      Pages 135-147

    13. Scaling Boosting by Margin-Based Inclusion of Features and Relations

      • Susanne Hoche, Stefan Wrobel
      Pages 148-160

    14. Multiclass Alternating Decision Trees

      • Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark Hall
      Pages 161-172

    15. Possibilistic Induction in Decision-Tree Learning

      • Eyke Hüllermeier
      Pages 173-184

    16. Improved Smoothing for Probabilistic Suffix Trees Seen as Variable Order Markov Chains

      • Christopher Kermorvant, Pierre Dupont
      Pages 185-194

    17. Collaborative Learning of Term-Based Concepts for Automatic Query Expansion

      • Stefan Klink, Armin Hust, Markus Junker, Andreas Dengel
      Pages 195-207

    18. Learning to Play a Highly Complex Game from Human Expert Games

      • Tony Kr°akenes, Ole Martin Halck
      Pages 207-218

    19. Reliable Classifications with Machine Learning

      • Matjaž Kukar, Igor Kononenko
      Pages 219-231
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Keywords

About this book

This book constitutes the refereed preceedings of the 13th European Conference on Machine Learning, ECML 2002, held in Helsinki, Finland in August 2002.
The 41 revised full papers presented together with 4 invited contributions were carefully reviewed and selected from numerous submissions. Among the topics covered are computational discovery, search strategies, Classification, support vector machines, kernel methods, rule induction, linear learning, decision tree learning, boosting, collaborative learning, statistical learning, clustering, instance-based learning, reinforcement learning, multiagent learning, multirelational learning, Markov decision processes, active learning, etc.

Editors and Affiliations

  • Department of Computer Science, University of Helsinki, Helsinki, Finland

    Tapio Elomaa, Heikki Mannila, Hannu Toivonen

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