Probabilistic inductive logic programming : theory and applications / Luc De Raedt [and others] (eds.).
Contributor(s): Raedt, Luc deMaterial type: TextSeries: SerienbezeichnungLNCS sublibrary: ; Lecture notes in computer science: 4911.; Lecture notes in computer science: ; Lecture notes in computer science: Publisher: Berlin ; New York : Springer, 2008Description: 1 online resource (viii, 339 pages) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9783540786528; 354078652X; 9783540786511; 3540786511Subject(s): Logic programming | Machine learning | Stochastic processes | Informatique | Logic programming | Machine learning | Stochastic processes | Computer Science | Mechanical Engineering - General | Engineering & Applied Sciences | Mechanical Engineering | algoritmen | algorithms | computeranalyse | computer analysis | bioinformatics | wiskunde | mathematics | programmeren | programming | computerwetenschappen | computer sciences | kunstmatige intelligentie | artificial intelligence | computational science | datamining | data mining | logica | logic | Information and Communication Technology (General) | Informatie- en communicatietechnologie (algemeen)Genre/Form: Electronic books. Additional physical formats: Print version:: Probabilistic inductive logic programming.DDC classification: 006.31 LOC classification: QA76.63 | .P69 2008ebOther classification: 004 | DAT 706f | SS 4800 Online resources: Click here to access online
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Includes bibliographical references and index.
"The question, how to combine probability and logic with learning, is gaining increased attention in several disciplines, e.g., knowledge representation, reasoning about uncertainty, data mining, and machine learning. The emerging field of study is known under the names of statistical relational learning and probabilistic inductive logic programming." "This book provides an introduction to the field with an emphasis on the methods based on logic programming principles. It is concerned with formalisms and systems, implementations and applications, as well as with the theory of probabilistic inductive logic programming."--Jacket.
Print version record.
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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