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ORCIDProbabilistic Inductive Logic Programming 2008
- Luc De Raedt
, Paolo Frasconi, Kristian Kersting, Stephen H. Muggleton:
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 M. 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 H. 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 H. Muggleton, Jianzhong Chen:
A Behavioral Comparison of Some Probabilistic Logic Models. 305-324 - Manfred Jaeger:
Model-Theoretic Expressivity Analysis. 325-339
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