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 III

  • Albert Bifet
  • Michael May
  • Bianca Zadrozny
  • Ricard Gavalda
  • Dino Pedreschi
  • Francesco Bonchi
  • Jaime Cardoso
  • Myra Spiliopoulou
Conference proceedings ECML PKDD 2015

DOI: 10.1007/978-3-319-23461-8

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9286)

Table of contents (43 papers)

  1. Front Matter
    Pages I-XXX
  2. Industrial Track

    1. Front Matter
      Pages 1-1
    2. Autonomous HVAC Control, A Reinforcement Learning Approach
      Enda Barrett, Stephen Linder
      Pages 3-19
    3. Country-Scale Exploratory Analysis of Call Detail Records Through the Lens of Data Grid Models
      Romain Guigourès, Dominique Gay, Marc Boullé, Fabrice Clérot, Fabrice Rossi
      Pages 37-52
    4. Early Detection of Fraud Storms in the Cloud
      Hani Neuvirth, Yehuda Finkelstein, Amit Hilbuch, Shai Nahum, Daniel Alon, Elad Yom-Tov
      Pages 53-67
    5. Learning Detector of Malicious Network Traffic from Weak Labels
      Vojtech Franc, Michal Sofka, Karel Bartos
      Pages 85-99
    6. Online Analysis of High-Volume Data Streams in Astroparticle Physics
      Christian Bockermann, Kai Brügge, Jens Buss, Alexey Egorov, Katharina Morik, Wolfgang Rhode et al.
      Pages 100-115
    7. Robust Representation for Domain Adaptation in Network Security
      Karel Bartos, Michal Sofka
      Pages 116-132
    8. Safe Exploration for Active Learning with Gaussian Processes
      Jens Schreiter, Duy Nguyen-Tuong, Mona Eberts, Bastian Bischoff, Heiner Markert, Marc Toussaint
      Pages 133-149
    9. Semi-Supervised Consensus Clustering for ECG Pathology Classification
      Helena Aidos, André Lourenço, Diana Batista, Samuel Rota Bulò, Ana Fred
      Pages 150-164
    10. Two Step graph-based semi-supervised Learning for Online Auction Fraud Detection
      Phiradet Bangcharoensap, Hayato Kobayashi, Nobuyuki Shimizu, Satoshi Yamauchi, Tsuyoshi Murata
      Pages 165-179
    11. Watch-It-Next: A Contextual TV Recommendation System
      Michal Aharon, Eshcar Hillel, Amit Kagian, Ronny Lempel, Hayim Makabee, Raz Nissim
      Pages 180-195
  3. Nectar Track

    1. Front Matter
      Pages 197-197
    2. Bayesian Hypothesis Testing in Machine Learning
      Giorgio Corani, Alessio Benavoli, Francesca Mangili, Marco Zaffalon
      Pages 199-202
    3. Data-Driven Exploration of Real-Time Geospatial Text Streams
      Harald Bosch, Robert Krüger, Dennis Thom
      Pages 203-207
    4. Discovering Neutrinos Through Data Analytics
      Mathis Börner, Wolfgang Rhode, Tim Ruhe, for the IceCube Collaboration, Katharina Morik
      Pages 208-212
    5. Logic-Based Incremental Process Mining
      Stefano Ferilli, Domenico Redavid, Floriana Esposito
      Pages 218-221

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

  • Albert Bifet
    • 1
  • Michael May
    • 2
  • Bianca Zadrozny
    • 3
  • Ricard Gavalda
    • 4
  • Dino Pedreschi
    • 5
  • Francesco Bonchi
    • 6
  • Jaime Cardoso
    • 7
  • Myra Spiliopoulou
    • 8
  1. 1.Huawei Noah’s Ark LabShatinHong Kong
  2. 2.Siemens AG Corporate TechnologyMünchenGermany
  3. 3.IBM Research BrazilRio de JaneiroBrazil
  4. 4.Universitat Politècnica de CatalunyaBarcelonaSpain
  5. 5.Università di PisaPisaItaly
  6. 6.Eurecat / Yahoo LabsBarcelonaSpain
  7. 7.University of Porto - INESC TECPortoPortugal
  8. 8.Otto-von-Guericke UniversityMagdeburgGermany

Bibliographic information

  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-23460-1
  • Online ISBN 978-3-319-23461-8
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349