LEADER 05728nam 22009135 450 001 996465749903316 005 20200704125606.0 010 $a3-540-78652-X 024 7 $a10.1007/978-3-540-78652-8 035 $a(CKB)1000000000490886 035 $a(SSID)ssj0000319571 035 $a(PQKBManifestationID)11246624 035 $a(PQKBTitleCode)TC0000319571 035 $a(PQKBWorkID)10338061 035 $a(PQKB)11576543 035 $a(DE-He213)978-3-540-78652-8 035 $a(MiAaPQ)EBC6280976 035 $a(MiAaPQ)EBC4975458 035 $a(MiAaPQ)EBC5576880 035 $a(Au-PeEL)EBL4975458 035 $a(CaONFJC)MIL134318 035 $a(OCoLC)1024280385 035 $a(Au-PeEL)EBL5576880 035 $a(OCoLC)1066183680 035 $a(PPN)123743842 035 $a(EXLCZ)991000000000490886 100 $a20100301d2008 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aProbabilistic Inductive Logic Programming$b[electronic resource] /$fedited by Luc De Raedt, Paolo Frasconi, Kristian Kersting, Stephen H. Muggleton 205 $a1st ed. 2008. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2008. 215 $a1 online resource (VIII, 341 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v4911 300 $aResearch report. 311 $a3-540-78651-1 320 $aIncludes bibliographical references and index. 327 $aProbabilistic 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. 330 $aThe question, how to combine probability and logic with learning, is getting an increased attention in several disciplines such as knowledge representation, reasoning about uncertainty, data mining, and machine learning simulateously. This results in the newly emerging subfield known under the names of statistical relational learning and probabilistic inductive logic programming. This book provides an introduction to the field with an emphasis on the methods based on logic programming principles. It is concerned with formalisms and systems, implementations and applications, as well as with the theory of probabilistic inductive logic programming. The 13 chapters of this state-of-the-art survey start with an introduction to probabilistic inductive logic programming; moreover the book presents a detailed overview of the most important probabilistic logic learning formalisms and systems such as relational sequence learning techniques, using kernels with logical representations, Markov logic, the PRISM system, CLP(BN), Bayesian logic programs, and the independent choice logic. The third part provides a detailed account of some show-case applications of probabilistic inductive logic programming. The final part touches upon some theoretical investigations and includes chapters on behavioural comparison of probabilistic logic programming representations and a model-theoretic expressivity analysis. 410 0$aLecture Notes in Artificial Intelligence ;$v4911 606 $aArtificial intelligence 606 $aComputer programming 606 $aMathematical logic 606 $aAlgorithms 606 $aData mining 606 $aBioinformatics 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aProgramming Techniques$3https://scigraph.springernature.com/ontologies/product-market-codes/I14010 606 $aMathematical Logic and Formal Languages$3https://scigraph.springernature.com/ontologies/product-market-codes/I16048 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aComputational Biology/Bioinformatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23050 615 0$aArtificial intelligence. 615 0$aComputer programming. 615 0$aMathematical logic. 615 0$aAlgorithms. 615 0$aData mining. 615 0$aBioinformatics. 615 14$aArtificial Intelligence. 615 24$aProgramming Techniques. 615 24$aMathematical Logic and Formal Languages. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aData Mining and Knowledge Discovery. 615 24$aComputational Biology/Bioinformatics. 676 $a005.1 702 $aDe Raedt$b Luc$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aFrasconi$b Paolo$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKersting$b Kristian$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMuggleton$b Stephen H$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465749903316 996 $aProbabilistic Inductive Logic Programming$9774421 997 $aUNISA