Skip to main content
Springer Nature Link
Log in

Probabilistic Inductive Logic Programming

  • Book
  • © 2008

Overview

Editors:
  1. Luc Raedt
    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Paolo Frasconi
    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Kristian Kersting
    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  4. Stephen Muggleton
    View editor publications

    You can also search for this editor in PubMed   Google Scholar

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

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Keywords

Table of contents (13 chapters)

  1. Front Matter

    Download chapter PDF

  2. Introduction

    1. Probabilistic Inductive Logic Programming

      • Luc De Raedt, Kristian Kersting
      Pages 1-27

  3. Formalisms and Systems

    1. Relational Sequence Learning

      • Kristian Kersting, Luc De Raedt, Bernd Gutmann, Andreas Karwath, Niels Landwehr
      Pages 28-55

    2. Learning with Kernels and Logical Representations

      • Paolo Frasconi, Andrea Passerini
      Pages 56-91

    3. Markov Logic

      • Pedro Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla
      Pages 92-117

    4. New Advances in Logic-Based Probabilistic Modeling by PRISM

      • Taisuke Sato, Yoshitaka Kameya
      Pages 118-155

    5. CLP(\(\cal{BN}\)): Constraint Logic Programming for Probabilistic Knowledge

      • Vítor Santos Costa, David Page, James Cussens
      Pages 156-188

    6. Basic Principles of Learning Bayesian Logic Programs

      • Kristian Kersting, Luc De Raedt
      Pages 189-221

    7. The Independent Choice Logic and Beyond

      • David Poole
      Pages 222-243

  4. Applications

    1. Protein Fold Discovery Using Stochastic Logic Programs

      • Jianzhong Chen, Lawrence Kelley, Stephen Muggleton, Michael Sternberg
      Pages 244-262

    2. Probabilistic Logic Learning from Haplotype Data

      • Niels Landwehr, Taneli Mielikäinen
      Pages 263-286

    3. Model Revision from Temporal Logic Properties in Computational Systems Biology

      • François Fages, Sylvain Soliman
      Pages 287-304

  5. Theory

    1. A Behavioral Comparison of Some Probabilistic Logic Models

      • Stephen Muggleton, Jianzhong Chen
      Pages 305-324

    2. Model-Theoretic Expressivity Analysis

      • Manfred Jaeger
      Pages 325-339

  6. Back Matter

    Download chapter PDF
Back to top

Bibliographic Information

Publish with us

Policies and ethics

Back to top