Probabilistic Inductive Logic Programming [electronic resource] : Theory and Applications / edited by Luc Raedt, Paolo Frasconi, Kristian Kersting, Stephen Muggleton.

Contributor(s): Raedt, Luc [editor.] | Frasconi, Paolo [editor.] | Kersting, Kristian [editor.] | Muggleton, Stephen [editor.] | SpringerLink (Online service)Material type: TextTextSeries: Lecture Notes in Computer Science ; 4911Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008Description: VIII, 341 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540786528Subject(s): Computer science | Computer software | Data mining | Artificial intelligence | Bioinformatics | Computer Science | Artificial Intelligence (incl. Robotics) | Programming Techniques | Mathematical Logic and Formal Languages | Algorithm Analysis and Problem Complexity | Data Mining and Knowledge Discovery | Computational Biology/BioinformaticsAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q334-342TJ210.2-211.495Online 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 eBooks
Item type: E-BOOKS
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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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