Machine Learning: ECML 2005

16th European Conference on Machine Learning, Porto, Portugal, October 3-7, 2005. Proceedings

  • João Gama
  • Rui Camacho
  • Pavel B. Brazdil
  • Alípio Mário Jorge
  • Luís Torgo
Conference proceedings ECML 2005
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3720)

Table of contents

  1. Front Matter
  2. Invited Talks

  3. Long Papers

    1. Christian Bessiere, Remi Coletta, Frédéric Koriche, Barry O’Sullivan
      Pages 23-34
    2. Steffen Bickel, Tobias Scheffer
      Pages 35-46
    3. Ulf Brefeld, Christoph Büscher, Tobias Scheffer
      Pages 60-71
    4. Jesús Cerquides, Ramon López de Mántaras
      Pages 72-83
    5. Jesse Davis, Elizabeth Burnside, Inês de Castro Dutra, David Page, Vítor Santos Costa
      Pages 84-95
    6. Isabel Drost, Tobias Scheffer
      Pages 96-107
    7. Arkady Epshteyn, Gerald DeJong
      Pages 108-119
    8. Stefano Ferilli, Teresa M. A. Basile, Nicola Di Mauro, Floriana Esposito
      Pages 120-132
    9. Vincent Guigue, Alain Rakotomamonjy, Stéphane Canu
      Pages 146-157
    10. Iris Hendrickx, Antal van den Bosch
      Pages 158-169

Other volumes

  1. Machine Learning: ECML 2005
    16th European Conference on Machine Learning, Porto, Portugal, October 3-7, 2005. Proceedings
  2. 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, October 3-7, 2005. Proceedings

About these proceedings

Introduction

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.

Keywords

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

Editors and affiliations

  • João Gama
    • 1
  • Rui Camacho
    • 2
  • Pavel B. Brazdil
    • 3
  • Alípio Mário Jorge
    • 4
  • Luís Torgo
    • 5
  1. 1.Faculty of Economics of the University of PortoPortugal
  2. 2.Faculdade de Engenharia & LIAADUniversidade do PortoPortugal
  3. 3.LIAAD-INESC Porto L.A./Faculty of EconomicsUniversity of PortoPortoPortugal
  4. 4.LIACC/FEPUniversidade do PortoPortugal
  5. 5.LIAAD-INESC Porto LA / FEPUniversity of PortoPortoPortugal

Bibliographic information

  • DOI https://doi.org/10.1007/11564096
  • Copyright Information Springer-Verlag Berlin Heidelberg 2005
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-29243-2
  • Online ISBN 978-3-540-31692-3
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book