05703nam 22009135 450 991048504810332120200704125606.03-540-78652-X10.1007/978-3-540-78652-8(CKB)1000000000490886(SSID)ssj0000319571(PQKBManifestationID)11246624(PQKBTitleCode)TC0000319571(PQKBWorkID)10338061(PQKB)11576543(DE-He213)978-3-540-78652-8(MiAaPQ)EBC6280976(MiAaPQ)EBC4975458(MiAaPQ)EBC5576880(Au-PeEL)EBL4975458(CaONFJC)MIL134318(OCoLC)1024280385(Au-PeEL)EBL5576880(OCoLC)1066183680(PPN)123743842(EXLCZ)99100000000049088620100301d2008 u| 0engurnn|008mamaatxtccrProbabilistic Inductive Logic Programming /edited by Luc De Raedt, Paolo Frasconi, Kristian Kersting, Stephen H. Muggleton1st ed. 2008.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2008.1 online resource (VIII, 341 p.) Lecture Notes in Artificial Intelligence ;4911Research report.3-540-78651-1 Includes bibliographical references and index.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.The question, how to combine probability and logic with learning, is getting an increased attention in several disciplines such as knowledge representation, reasoning about uncertainty, data mining, and machine learning simulateously. This results in the newly emerging subfield 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. The 13 chapters of this state-of-the-art survey start with an introduction to probabilistic inductive logic programming; moreover the book presents a detailed overview of the most important probabilistic logic learning formalisms and systems such as relational sequence learning techniques, using kernels with logical representations, Markov logic, the PRISM system, CLP(BN), Bayesian logic programs, and the independent choice logic. The third part provides a detailed account of some show-case applications of probabilistic inductive logic programming. The final part touches upon some theoretical investigations and includes chapters on behavioural comparison of probabilistic logic programming representations and a model-theoretic expressivity analysis.Lecture Notes in Artificial Intelligence ;4911Artificial intelligenceComputer programmingMathematical logicAlgorithmsData miningBioinformaticsArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Programming Techniqueshttps://scigraph.springernature.com/ontologies/product-market-codes/I14010Mathematical Logic and Formal Languageshttps://scigraph.springernature.com/ontologies/product-market-codes/I16048Algorithm Analysis and Problem Complexityhttps://scigraph.springernature.com/ontologies/product-market-codes/I16021Data Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Computational Biology/Bioinformaticshttps://scigraph.springernature.com/ontologies/product-market-codes/I23050Artificial intelligence.Computer programming.Mathematical logic.Algorithms.Data mining.Bioinformatics.Artificial Intelligence.Programming Techniques.Mathematical Logic and Formal Languages.Algorithm Analysis and Problem Complexity.Data Mining and Knowledge Discovery.Computational Biology/Bioinformatics.005.1De Raedt Lucedthttp://id.loc.gov/vocabulary/relators/edtFrasconi Paoloedthttp://id.loc.gov/vocabulary/relators/edtKersting Kristianedthttp://id.loc.gov/vocabulary/relators/edtMuggleton Stephen Hedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK9910485048103321Probabilistic Inductive Logic Programming774421UNINA