Luc De Raedt, Paolo Frasconi, Kristian Kersting, Stephen Muggleton (Eds.):
Probabilistic Inductive Logic Programming - Theory and Applications.
Lecture Notes in Computer Science 4911 Springer 2008, ISBN 978-3-540-78651-1 
- Luc De Raedt, Kristian Kersting:
Probabilistic Inductive Logic Programming.
1-27
- Kristian Kersting, Luc De Raedt, Bernd Gutmann, Andreas Karwath, Niels Landwehr:
Relational Sequence Learning.
28-55
- Paolo Frasconi, Andrea Passerini:
Learning with Kernels and Logical Representations.
56-91
- Pedro Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla:
Markov Logic.
92-117
- Taisuke Sato, Yoshitaka Kameya:
New Advances in Logic-Based Probabilistic Modeling by PRISM.
118-155
- Vítor Santos Costa, David Page, James Cussens:
CLP(BN): Constraint Logic Programming for Probabilistic Knowledge.
156-188
- Kristian Kersting, Luc De Raedt:
Basic Principles of Learning Bayesian Logic Programs.
189-221
- David Poole:
The Independent Choice Logic and Beyond.
222-243
- Jianzhong Chen, Lawrence A. Kelley, Stephen Muggleton, Michael J. E. Sternberg:
Protein Fold Discovery Using Stochastic Logic Programs.
244-262
- Niels Landwehr, Taneli Mielikäinen:
Probabilistic Logic Learning from Haplotype Data.
263-286
- François Fages, Sylvain Soliman:
Model Revision from Temporal Logic Properties in Computational Systems Biology.
287-304
- Stephen Muggleton, Jianzhong Chen:
A Behavioral Comparison of Some Probabilistic Logic Models.
305-324
- Manfred Jaeger:
Model-Theoretic Expressivity Analysis.
325-339
Last update Fri May 25 08:22:40 2012
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