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Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part I

  • Hendrik Blockeel
  • Kristian Kersting
  • Siegfried Nijssen
  • Filip Železný

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8188)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 8188)

Table of contents

  1. Front Matter
  2. Reinforcement Learning

    1. Edouard Klein, Bilal Piot, Matthieu Geist, Olivier Pietquin
      Pages 1-16
    2. Bilal Piot, Matthieu Geist, Olivier Pietquin
      Pages 17-32
    3. Bastian Bischoff, Duy Nguyen-Tuong, Torsten Koller, Heiner Markert, Alois Knoll
      Pages 49-64
    4. Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill
      Pages 97-112
    5. Robert Wright, Steven Loscalzo, Philip Dexter, Lei Yu
      Pages 113-128
  3. Markov Decision Processes

    1. Joni Pajarinen, Jaakko Peltonen
      Pages 129-144
    2. Caroline P. Carvalho Chanel, Florent Teichteil-Königsbuch
      Pages 145-161
    3. Omar Zia Khan, Pascal Poupart, John Mark Agosta
      Pages 162-177
    4. Saket Joshi, Roni Khardon, Prasad Tadepalli, Aswin Raghavan, Alan Fern
      Pages 178-193
    5. David Auger, Adrien Couëtoux, Olivier Teytaud
      Pages 194-209
  4. Active Learning and Optimization

    1. Ali Jalali, Javad Azimi, Xiaoli Fern, Ruofei Zhang
      Pages 210-224
    2. Emile Contal, David Buffoni, Alexandre Robicquet, Nicolas Vayatis
      Pages 225-240
    3. José Bento, Stratis Ioannidis, S. Muthukrishnan, Jinyun Yan
      Pages 257-272
    4. Meng Fang, Jie Yin, Xingquan Zhu
      Pages 273-288
  5. Learning from Sequences

    1. Adrià Recasens, Ariadna Quattoni
      Pages 289-304
    2. Ralf Eggeling, André Gohr, Pierre-Yves Bourguignon, Edgar Wingender, Ivo Grosse
      Pages 321-336
    3. Andreas Henelius, Jussi Korpela, Kai Puolamäki
      Pages 337-352
    4. Cheng Zhou, Boris Cule, Bart Goethals
      Pages 353-368
    5. Henrik Grosskreutz, Bastian Lang, Daniel Trabold
      Pages 369-384
    6. Sam Blasiak, Huzefa Rangwala, Kathryn B. Laskey
      Pages 401-416
  6. Time Series and Spatio-temporal Data

    1. Disheng Qiu, Paolo Papotti, Lorenzo Blanco
      Pages 417-432
  7. Data Streams

    1. Indrė Žliobaitė, Jaakko Hollmén
      Pages 449-464
    2. Albert Bifet, Jesse Read, Indrė Žliobaitė, Bernhard Pfahringer, Geoff Holmes
      Pages 465-479
    3. Ezilda Almeida, Carlos Ferreira, João Gama
      Pages 480-492
    4. Reza Akbarinia, Florent Masseglia
      Pages 493-508
  8. Graphs and Networks

    1. Jan Ramon, Pauli Miettinen, Jilles Vreeken
      Pages 509-524
    2. Petko Bogdanov, Ben Baumer, Prithwish Basu, Amotz Bar-Noy, Ambuj K. Singh
      Pages 525-540
    3. Abhijin Adiga, Anil Kumar S. Vullikanti
      Pages 541-556
    4. Guoxian Yu, Carlotta Domeniconi, Huzefa Rangwala, Guoji Zhang
      Pages 574-589
    5. Cristina Pérez-Solà, Jordi Herrera-Joancomartí
      Pages 590-605
    6. Tamás Horváth, Keisuke Otaki, Jan Ramon
      Pages 622-637
    7. Elena Valari, Apostolos N. Papadopoulos
      Pages 638-653
    8. Elise Desmier, Marc Plantevit, Céline Robardet, Jean-François Boulicaut
      Pages 654-669
    9. Aonan Zhang, Jun Zhu, Bo Zhang
      Pages 670-685
  9. Back Matter

Other volumes

  1. Machine Learning and Knowledge Discovery in Databases
    European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part I
  2. European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II
  3. European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part III

About these proceedings

Introduction

This three-volume set LNAI 8188, 8189 and 8190 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2013, held in Prague, Czech Republic, in September 2013. The 111 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 447 submissions. The papers are organized in topical sections on reinforcement learning; Markov decision processes; active learning and optimization; learning from sequences; time series and spatio-temporal data; data streams; graphs and networks; social network analysis; natural language processing and information extraction; ranking and recommender systems; matrix and tensor analysis; structured output prediction, multi-label and multi-task learning; transfer learning; bayesian learning; graphical models; nearest-neighbor methods; ensembles; statistical learning; semi-supervised learning; unsupervised learning; subgroup discovery, outlier detection and anomaly detection; privacy and security; evaluation; applications; medical applications; nectar track; demo track.

Keywords

bayesian network data mining graph-based methods parallel optimization social responsibility

Editors and affiliations

  • Hendrik Blockeel
    • 1
  • Kristian Kersting
    • 2
  • Siegfried Nijssen
    • 3
  • Filip Železný
    • 4
  1. 1.Department of Computer ScienceKatholieke Universiteit LeuvenLeuvenBelgium
  2. 2.Fraunhofer IAIS, Department of Knowledge DiscoveryUniversity of BonnSankt AugustinGermany
  3. 3.LIACSUniversiteit LeidenLeidenThe Netherlands
  4. 4.Department of Computer Science and EngineeringCzech Technical UniversityPrague 6Czech Republic

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-40988-2
  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-40987-5
  • Online ISBN 978-3-642-40988-2
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site