ECML PKDD: Joint European Conference on Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part I

  • Annalisa Appice
  • Pedro Pereira Rodrigues
  • Vítor Santos Costa
  • Carlos Soares
  • João Gama
  • Alípio Jorge
Conference proceedings ECML PKDD 2015

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9284)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 9284)

Table of contents

  1. Front Matter
    Pages I-LVIII
  2. Research Track - Classification, Regression and Supervised Learning

    1. Front Matter
      Pages 1-1
    2. Vikas C. Raykar, Amrita Saha
      Pages 3-19
    3. Rana Haber, Anand Rangarajan, Adrian M. Peter
      Pages 20-36
    4. Paul Mineiro, Nikos Karampatziakis
      Pages 37-51
    5. Wojciech Marian Czarnecki, Rafal Jozefowicz, Jacek Tabor
      Pages 52-67
    6. Jinseok Nam, Eneldo Loza Mencía, Hyunwoo J. Kim, Johannes Fürnkranz
      Pages 102-118
    7. Wendelin Böhmer, Klaus Obermayer
      Pages 119-134
    8. Yutaro Shigeto, Ikumi Suzuki, Kazuo Hara, Masashi Shimbo, Yuji Matsumoto
      Pages 135-151
    9. Michael Großhans, Tobias Scheffer
      Pages 152-167
    10. Martin Ratajczak, Sebastian Tschiatschek, Franz Pernkopf
      Pages 168-183
    11. Reem Al-Otaibi, Ricardo B. C. Prudêncio, Meelis Kull, Peter Flach
      Pages 184-199
    12. Andrea Dal Pozzolo, Olivier Caelen, Gianluca Bontempi
      Pages 200-215
  3. Clustering and Unsupervised Learning

    1. Front Matter
      Pages 217-217
    2. Yuanli Pei, Li-Ping Liu, Xiaoli Z. Fern
      Pages 235-250
    3. Markus Ring, Florian Otto, Martin Becker, Thomas Niebler, Dieter Landes, Andreas Hotho
      Pages 251-266
    4. Junting Ye, Leman Akoglu
      Pages 267-282

About these proceedings


The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015.

The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, and 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.


data mining foundations of machine learning and data mining knowledge discovery in databases probabilistic models and statistical methods social and graphs mining classification, regression and supervised learning clustering and unsupervised learning domain adaptation ensemble learning large scale learning and big data learning paradigms machine learning and data mining applications machine learning methodologies meta-learning nonmonotonic constraints pattern and sequence mining privacy-preserving data mining probabilistic programming recommender systems rich data mining

Editors and affiliations

  • Annalisa Appice
    • 1
  • Pedro Pereira Rodrigues
    • 2
  • Vítor Santos Costa
    • 3
  • Carlos Soares
    • 4
  • João Gama
    • 5
  • Alípio Jorge
    • 6
  1. 1.University of Bari Aldo MoroBariItaly
  2. 2.University of PortoPortoPortugal
  3. 3.University of Porto - CRACS/INESC TECPortoPortugal
  4. 4.University of Porto - INESC TECPortoPortugal
  5. 5.University of Porto - INESC TECPortoPortugal
  6. 6.University of Porto - INESC TECPortoPortugal

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-23527-1
  • Online ISBN 978-3-319-23528-8
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book