Probabilistic Inductive Logic Programming (eBook, PDF)

Redaktion: De Raedt, Luc; Muggleton, Stephen H.; Kersting, Kristian; Frasconi, Paolo

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 341
• Erscheinungstermin: 26. Februar 2008
• Englisch
• ISBN-13: 9783540786528
• Artikelnr.: 44131941

Inhaltsangabe

Probabilistic Inductive Logic Programming.- Formalisms and Systems.- Relational Sequence Learning.- Learning with Kernels and Logical Representations.- Markov Logic.- New Advances in Logic-Based Probabilistic Modeling by PRISM.- CLP( ): Constraint Logic Programming for Probabilistic Knowledge.- Basic Principles of Learning Bayesian Logic Programs.- The Independent Choice Logic and Beyond.- Applications.- Protein Fold Discovery Using Stochastic Logic Programs.- Probabilistic Logic Learning from Haplotype Data.- Model Revision from Temporal Logic Properties in Computational Systems Biology.- Theory.- A Behavioral Comparison of Some Probabilistic Logic Models.- Model-Theoretic Expressivity Analysis.