Inductive Logic Programming

20th International Conference, ILP 2010, Florence, Italy, June 27-30, 2010. Revised Papers

  • Paolo Frasconi
  • Francesca A. Lisi
Conference proceedings ILP 2010

DOI: 10.1007/978-3-642-21295-6

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6489)

Table of contents

  1. Front Matter
  2. Abstracts of Invited Talks

  3. Research Papers

    1. Tarek Abudawood, Peter A. Flach
      Pages 6-13
    2. Erick Alphonse, Tobias Girschick, Fabian Buchwald, Stefan Kramer
      Pages 14-21
    3. Laura Antanas, Martijn van Otterlo, José Oramas M., Tinne Tuytelaars, Luc De Raedt
      Pages 22-29
    4. Stefano Bragaglia, Fabrizio Riguzzi
      Pages 30-37
    5. Luc De Raedt, Ingo Thon
      Pages 47-58
    6. Nuno A. Fonseca, Max Pereira, Vítor Santos Costa, Rui Camacho
      Pages 59-66
    7. Beatriz García-Jiménez, Agapito Ledezma, Araceli Sanchis
      Pages 67-75
    8. Bernd Gutmann, Manfred Jaeger, Luc De Raedt
      Pages 76-91
    9. Yi Huang, Volker Tresp, Markus Bundschus, Achim Rettinger, Hans-Peter Kriegel
      Pages 92-104
    10. Alberto Illobre, Jorge Gonzalez, Ramon Otero, Jose Santos
      Pages 105-113
    11. Katsumi Inoue, Andrei Doncescu, Hidetomo Nabeshima
      Pages 114-129
    12. Srihari Kalgi, Chirag Gosar, Prasad Gawde, Ganesh Ramakrishnan, Kekin Gada, Chander Iyer et al.
      Pages 130-137
    13. Matthieu Lopez, Lionel Martin, Christel Vrain
      Pages 146-157
    14. Stephen H. Muggleton, Jianzhong Chen, Hiroaki Watanabe, Stuart J. Dunbar, Charles Baxter, Richard Currie et al.
      Pages 158-170

About these proceedings

Introduction

This book constitutes the thoroughly refereed post-proceedings of the 20th International Conference on Inductive Logic Programming, ILP 2010, held in Florence, Italy in June 2010. The 11 revised full papers and 15 revised short papers presented together with abstracts of three invited talks were carefully reviewed and selected during two rounds of refereeing and revision. All current issues in inductive logic programming, i.e. in logic programming for machine learning are addressed, in particular statistical learning and other probabilistic approaches to machine learning are reflected.

Keywords

algorithmic learning approximate inference computational learning data mining probabilistic programming relational learning rule learning

Editors and affiliations

  • Paolo Frasconi
    • 1
  • Francesca A. Lisi
    • 2
  1. 1.Dipartimento di Sistemi e InformaticaUniversitá degli Studi di FirenzeItaly
  2. 2.Dipartimento di InformaticaUniversità degli Studi di BariBariItaly

Bibliographic information

  • Copyright Information Springer Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-21294-9
  • Online ISBN 978-3-642-21295-6
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349