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Probabilistic Inductive Logic Programming

Theory and Applications

  • Editors
  • Luc De Raedt
  • Paolo Frasconi
  • Kristian Kersting
  • Stephen Muggleton

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4911)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 4911)

Table of contents

  1. Front Matter
  2. Introduction

    1. Luc De Raedt, Kristian Kersting
      Pages 1-27
  3. Formalisms and Systems

    1. Kristian Kersting, Luc De Raedt, Bernd Gutmann, Andreas Karwath, Niels Landwehr
      Pages 28-55
    2. Paolo Frasconi, Andrea Passerini
      Pages 56-91
    3. Pedro Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla
      Pages 92-117
    4. Taisuke Sato, Yoshitaka Kameya
      Pages 118-155
    5. Vítor Santos Costa, David Page, James Cussens
      Pages 156-188
    6. Kristian Kersting, Luc De Raedt
      Pages 189-221
    7. David Poole
      Pages 222-243
  4. Applications

    1. Jianzhong Chen, Lawrence Kelley, Stephen Muggleton, Michael Sternberg
      Pages 244-262
    2. Niels Landwehr, Taneli Mielikäinen
      Pages 263-286
  5. Theory

    1. Stephen Muggleton, Jianzhong Chen
      Pages 305-324
    2. Manfred Jaeger
      Pages 325-339
  6. Back Matter

About this book

Keywords

Bayesian networks Kernel algorithmic learning classifier systems clustering computational biology constraint logic programming genetic programming inductive logic programmi knowledge learning logic logic programming machine learning programming

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-78652-8
  • Copyright Information Springer-Verlag Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-78651-1
  • Online ISBN 978-3-540-78652-8
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site