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 II

  • Annalisa Appice
  • Pedro Pereira Rodrigues
  • Vítor Santos Costa
  • João Gama
  • Alípio Jorge
  • Carlos Soares

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9285)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 9285)

Table of contents

  1. Front Matter
    Pages I-XLII
  2. Matrix and Tensor Analysis

    1. Front Matter
      Pages 1-1
    2. Nipa Chowdhury, Xiongcai Cai, Cheng Luo
      Pages 3-18
    3. Mathieu Blondel, Akinori Fujino, Naonori Ueda
      Pages 19-35
    4. Changwei Hu, Piyush Rai, Changyou Chen, Matthew Harding, Lawrence Carin
      Pages 53-70
    5. Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme
      Pages 87-103
    6. Niloofar Yousefi, Michael Georgiopoulos, Georgios C. Anagnostopoulos
      Pages 120-136
    7. Marina M.-C. Vidovic, Nico Görnitz, Klaus-Robert Müller, Gunnar Rätsch, Marius Kloft
      Pages 137-153
  3. Pattern and Sequence Mining

    1. Front Matter
      Pages 155-155
    2. Aleksey Buzmakov, Sergei O. Kuznetsov, Amedeo Napoli
      Pages 157-172
    3. Hoang-Vu Nguyen, Jilles Vreeken
      Pages 173-189
    4. Nicolas Méger, Christophe Rigotti, Catherine Pothier
      Pages 190-205
  4. Preference Learning and Label Ranking

    1. Front Matter
      Pages 225-225
    2. Dirk Schäfer, Eyke Hüllermeier
      Pages 227-242
    3. Sebastian Pölsterl, Nassir Navab, Amin Katouzian
      Pages 243-259
    4. Eyke Hüllermeier, Weiwei Cheng
      Pages 260-275

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, 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
  • João Gama
    • 4
  • Alípio Jorge
    • 5
  • Carlos Soares
    • 6
  1. 1.University of Bari Aldo MoroBariItaly
  2. 2.University of PortoPortoPortugal
  3. 3.Universidade do PortoPortoPortugal
  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 978-3-319-23524-0
  • Online ISBN 978-3-319-23525-7
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book