Probabilistic Inductive Logic Programming

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Luc De Raedt, Paolo Frasconi, Kristian Kersting, Stephen H. Muggleton
Springer, 26.02.2008 - 341 Seiten
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This book provides an introduction to probabilistic inductive logic programming. It places emphasis on the methods based on logic programming principles and covers formalisms and systems, implementations and applications, as well as theory.

 

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Inhalt

Probabilistic Inductive Logic Programming
1
Relational Sequence Learning
28
Learning with Kernels and Logical Representations
56
Markov Logic
92
New Advances in LogicBased Probabilistic Modeling by PRISM
118
Constraint Logic Programming for Probabilistic Knowledge
156
Basic Principles of Learning Bayesian Logic Programs
189
The Independent Choice Logic and Beyond
222
Protein Fold Discovery Using Stochastic Logic Programs
244
Probabilistic Logic Learning from Haplotype Data
263
Model Revision from Temporal Logic Properties in Computational Systems Biology
287
A Behavioral Comparison of Some Probabilistic Logic Models
305
ModelTheoretic Expressivity Analysis
325
Author Index
340
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