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© 2006

Machine Learning: ECML 2006

17th European Conference on Machine Learning Berlin, Germany, September 18-22, 2006 Proceedings

  • Johannes Fürnkranz
  • Tobias Scheffer
  • Myra Spiliopoulou
Conference proceedings ECML 2006

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4212)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 4212)

Table of contents

  1. Front Matter
  2. Invited Talks

    1. Charu C. Aggarwal
      Pages 1-1
    2. C. Lee Giles
      Pages 2-2
    3. Jonathan Schaeffer
      Pages 3-3
    4. Sebastian Thrun
      Pages 4-4
    5. Henry Tirri
      Pages 5-5
  3. Long Papers

    1. Alon Altman, Avivit Bercovici-Boden, Moshe Tennenholtz
      Pages 6-17
    2. Massih Amini, Nicolas Usunier, François Laviolette, Alexandre Lacasse, Patrick Gallinari
      Pages 18-29
    3. Ron Bekkerman, Mehran Sahami, Erik Learned-Miller
      Pages 30-41
    4. Marc Bernard, Amaury Habrard, Marc Sebban
      Pages 42-53
    5. Christopher H. Bryant, Daniel C. Fredouille, Alex Wilson, Channa K. Jayawickreme, Steven Jupe, Simon Topp
      Pages 54-65
    6. Jérôme Callut, Pierre Dupont
      Pages 78-89
    7. Alexander Clark, Christophe Costa Florêncio, Chris Watkins
      Pages 90-101
    8. Uwe Dick, Kristian Kersting
      Pages 114-125
    9. William Elazmeh, Nathalie Japkowicz, Stan Matwin
      Pages 126-137
    10. Raquel Fuentetaja, Daniel Borrajo
      Pages 138-149
    11. Ricard Gavaldà, Philipp W. Keller, Joelle Pineau, Doina Precup
      Pages 150-161
    12. David Grangier, Florent Monay, Samy Bengio
      Pages 162-173

Other volumes

  1. Machine Learning: ECML 2006
    17th European Conference on Machine Learning Berlin, Germany, September 18-22, 2006 Proceedings
  2. 10th European Conference on Principles and Practice of Knowledge Discovery in Databases Berlin, Germany, September 18-22, 2006 Proceedings

About these proceedings

Keywords

Boosting Support Vector Machine active learning algorithm algorithmic learning algorithms case-based learning classifier systems clustering algorithms knowledge discovery learning logic machine learning multiple-instance learning sensing

Editors and affiliations

  • Johannes Fürnkranz
    • 1
  • Tobias Scheffer
    • 2
  • Myra Spiliopoulou
    • 3
  1. 1.Knowledge Engineering GroupTechnische Universität Darmstadt 
  2. 2.Max Planck Institute for Computer ScienceSaarbrückenGermany
  3. 3.Faculty of Computer ScienceOtto-von-Guericke-University MagdeburgGermany

Bibliographic information

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Reviews

From the reviews:

"In this book, we find many ways of representing machine learning from different fields, including active learning, algorithmic learning, case-based learning, classifier systems, clustering algorithms, decision-tree learning, inductive inference, kernel methods, knowledge discovery, multiple-instance learning, reinforcement learning, statistical learning, and support vector machines. Most of the current issues in machine learning research are discussed. … I strongly recommend this book for all researchers interested in the very best of machine learning studies." (Agliberto Cierco, ACM Computing Reviews, Vol. 49 (5), 2008)