Machine Learning: ECML 2005

1. Front Matter

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2. Invited Talks

   1. Data Analysis in the Life Sciences — Sparking Ideas —

      • Michael R. Berthold

      Pages 1-1

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   2. Machine Learning for Natural Language Processing (and Vice Versa?)

      • Claire Cardie

      Pages 2-2

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   3. Statistical Relational Learning: An Inductive Logic Programming Perspective

      • Luc De Raedt

      Pages 3-5

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   4. Recent Advances in Mining Time Series Data

      • Eamonn Keogh

      Pages 6-6

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   5. Focus the Mining Beacon: Lessons and Challenges from the World of E-Commerce

      • Ron Kohavi

      Pages 7-7

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   6. Data Streams and Data Synopses for Massive Data Sets (Invited Talk)

      • Yossi Matias

      Pages 8-9

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3. Long Papers

   1. Clustering and Metaclustering with Nonnegative Matrix Decompositions

      • Liviu Badea

      Pages 10-22

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   2. A SAT-Based Version Space Algorithm for Acquiring Constraint Satisfaction Problems

      • Christian Bessiere, Remi Coletta, Frédéric Koriche, Barry O’Sullivan

      Pages 23-34

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   3. Estimation of Mixture Models Using Co-EM

      • Steffen Bickel, Tobias Scheffer

      Pages 35-46

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   4. Nonrigid Embeddings for Dimensionality Reduction

      • Matthew Brand

      Pages 47-59

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   5. Multi-view Discriminative Sequential Learning

      • Ulf Brefeld, Christoph Büscher, Tobias Scheffer

      Pages 60-71

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   6. Robust Bayesian Linear Classifier Ensembles

      • Jesús Cerquides, Ramon López de Mántaras

      Pages 72-83

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   7. An Integrated Approach to Learning Bayesian Networks of Rules

      • Jesse Davis, Elizabeth Burnside, Inês de Castro Dutra, David Page, Vítor Santos Costa

      Pages 84-95

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   8. Thwarting the Nigritude Ultramarine: Learning to Identify Link Spam

      • Isabel Drost, Tobias Scheffer

      Pages 96-107

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   9. Rotational Prior Knowledge for SVMs

      • Arkady Epshteyn, Gerald DeJong

      Pages 108-119

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   10. On the LearnAbility of Abstraction Theories from Observations for Relational Learning

       • Stefano Ferilli, Teresa M. A. Basile, Nicola Di Mauro, Floriana Esposito

       Pages 120-132

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   11. Beware the Null Hypothesis: Critical Value Tables for Evaluating Classifiers

       • George Forman, Ira Cohen

       Pages 133-145

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   12. Kernel Basis Pursuit

       • Vincent Guigue, Alain Rakotomamonjy, Stéphane Canu

       Pages 146-157

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   13. Hybrid Algorithms with Instance-Based Classification

       • Iris Hendrickx, Antal van den Bosch

       Pages 158-169

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The European Conference on Machine Learning (ECML) and the European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) were jointly organized this year for the ?fth time in a row, after some years of mutual independence before. After Freiburg (2001), Helsinki (2002), Cavtat (2003) and Pisa (2004), Porto received the 16th edition of ECML and the 9th PKDD in October 3–7. Having the two conferences together seems to be working well: 585 di?erent paper submissions were received for both events, which maintains the high s- mission standard of last year. Of these, 335 were submitted to ECML only, 220 to PKDD only and 30 to both. Such a high volume of scienti?c work required a tremendous e?ort from Area Chairs, Program Committee members and some additional reviewers. On average, PC members had 10 papers to evaluate, and Area Chairs had 25 papers to decide upon. We managed to have 3 highly qua- ?edindependentreviewsperpaper(withveryfewexceptions)andoneadditional overall input from one of the Area Chairs. After the authors’ responses and the online discussions for many of the papers, we arrived at the ?nal selection of 40 regular papers for ECML and 35 for PKDD. Besides these, 32 others were accepted as short papers for ECML and 35 for PKDD. This represents a joint acceptance rate of around 13% for regular papers and 25% overall. We thank all involved for all the e?ort with reviewing and selection of papers. Besidesthecoretechnicalprogram,ECMLandPKDDhad6invitedspeakers, 10 workshops, 8 tutorials and a Knowledge Discovery Challenge.

• Bayesian network
• Boosting
• Hidden Markov Model
• Markov decision process
• Support Vector Machine
• algorithmic learning
• association rule mining
• case-based learning
• classifier systems
• inductive inference
• k-Means
• knowledge discovery
• machine learning
• reinforcement learning
• statistical learning
• algorithm analysis and problem complexity

• Faculty of Economics of the University of Porto, Portugal

  João Gama

• Faculdade de Engenharia & LIAAD, Universidade do Porto, Portugal

  Rui Camacho

• LIAAD-INESC Porto L.A./Faculty of Economics, University of Porto, Porto, Portugal

  Pavel B. Brazdil

• LIACC/FEP, Universidade do Porto, Portugal

  Alípio Mário Jorge

• LIAAD-INESC Porto LA / FEP, University of Porto, Porto, Portugal

  Luís Torgo

Policies and ethics