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Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011. Proceedings, Part I

  • Dimitrios Gunopulos
  • Thomas Hofmann
  • Donato Malerba
  • Michalis Vazirgiannis

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6911)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 6911)

Table of contents

  1. Regular Papers

    1. Maria Halkidi, Iordanis Koutsopoulos
      Pages 629-644
  2. Back Matter

Other volumes

  1. Machine Learning and Knowledge Discovery in Databases
    European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011. Proceedings, Part I
  2. European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II
  3. European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part III

About these proceedings

Introduction

This three-volume set LNAI 6911, LNAI 6912, and LNAI 6913 constitutes the refereed proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2011, held in Athens, Greece, in September 2011. The 121 revised full papers presented together with 10 invited talks and 11 demos in the three volumes, were carefully reviewed and selected from about 600 paper submissions. The papers address all areas related to machine learning and knowledge discovery in databases as well as other innovative application domains such as supervised and unsupervised learning with some innovative contributions in fundamental issues; dimensionality reduction, distance and similarity learning, model learning and matrix/tensor analysis; graph mining, graphical models, hidden markov models, kernel methods, active and ensemble learning, semi-supervised and transductive learning, mining sparse representations, model learning, inductive logic programming, and statistical learning. a significant part of the papers covers novel and timely applications of data mining and machine learning in industrial domains.

Keywords

decision theory high-dimensional clustering natural language processing recommender systems self-organizing maps

Editors and affiliations

  • Dimitrios Gunopulos
    • 1
  • Thomas Hofmann
    • 2
  • Donato Malerba
    • 3
  • Michalis Vazirgiannis
    • 4
  1. 1.Department of Informatics and TelecommunicationsUniversity of AthensAthensGreece
  2. 2.Google Switzerland GmbHZurichSwitzerland
  3. 3.Department of Computer ScienceUniversity of Bari “Aldo Moro”BariItaly
  4. 4.Deptartment of InformaticsAthens University of Economics and BusinessAthensGreece

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-23780-5
  • Copyright Information Springer-Verlag GmbH Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-23779-9
  • Online ISBN 978-3-642-23780-5
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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