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Probabilistic Inductive Logic Programming [electronic resource] / edited by Luc De Raedt, Paolo Frasconi, Kristian Kersting, Stephen H. Muggleton.

Contributor(s): De Raedt, Luc [editor.] | Frasconi, Paolo [editor.] | Kersting, Kristian [editor.] | Muggleton, Stephen H [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Artificial Intelligence: 4911Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008Edition: 1st ed. 2008.Description: VIII, 341 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540786528.Subject(s): Artificial intelligence | Computer programming | Machine theory | Algorithms | Data mining | Bioinformatics | Artificial Intelligence | Programming Techniques | Formal Languages and Automata Theory | Algorithms | Data Mining and Knowledge Discovery | Computational and Systems BiologyAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
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.
In: Springer Nature eBook
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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.

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