Probabilistic Inductive Logic Programming (Lecture Notes in Computer Science)

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9783540786511: Probabilistic Inductive Logic Programming (Lecture Notes in Computer Science)

This book provides an introduction to probabilistic inductive logic programming. It places emphasis on the methods based on logic programming principles and covers formalisms and systems, implementations and applications, as well as theory.

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Luc de Raedt
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Luc De Raedt
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LUC DE RAEDT
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De Raedt, Luc
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Book Description Springer, 2016. Paperback. Book Condition: New. PRINT ON DEMAND Book; New; Publication Year 2016; Not Signed; Fast Shipping from the UK. No. book. Bookseller Inventory # ria9783540786511_lsuk

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Luc De Raedt (editor), Paolo Frasconi (editor), Kristian Kersting (editor), Stephen H. Muggleton (editor)
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Book Description Springer. Paperback. Book Condition: New. Paperback. 341 pages. Dimensions: 9.3in. x 6.1in. x 0.9in.One of the key open questions within articial intelligence is how to combine probability and logic with learning. This question is getting an increased - tentioninseveraldisciplinessuchasknowledgerepresentation, reasoningabout uncertainty, data mining, and machine learning simulateously, resulting in the newlyemergingsubeldknownasstatisticalrelationallearningandprobabil- ticinductivelogicprogramming. Amajordriving forceisthe explosivegrowth in the amount of heterogeneous data that is being collected in the business and scientic world. Example domains include bioinformatics, chemoinform- ics, transportation systems, communication networks, social network analysis, linkanalysis, robotics, amongothers. Thestructuresencounteredcanbeass- pleassequencesandtrees(suchasthosearisinginproteinsecondarystructure predictionandnaturallanguageparsing)orascomplexascitationgraphs, the WorldWideWeb, andrelationaldatabases. This book providesan introduction to this eld with an emphasison those methods based on logic programming principles. The book is also the main resultofthesuccessfulEuropeanISTFETprojectno. FP6-508861onAppli- tionofProbabilisticInductiveLogicProgramming(APRILII, 2004-2007). This projectwascoordinatedbytheAlbertLudwigsUniversityofFreiburg(Ger many, Luc De Raedt) and the partners were Imperial College London (UK, Stephen MuggletonandMichaelSternberg), theHelsinkiInstituteofInformationTe- nology(Finland, HeikkiMannila), theUniversit adegliStudidiFlorence(Italy, PaoloFrasconi), andtheInstitutNationaldeRechercheenInformatiqueet- tomatiqueRocquencourt(France, FrancoisFages). Itwasconcernedwiththeory, implementationsandapplicationsofprobabilisticinductivelogicp rogramming. Thisstructureisalsoreectedinthebook. The book starts with an introductory chapter to Probabilistic Inductive LogicProgrammingbyDeRaedtandKersting. Inasecondpart, itprovidesa detailedoverviewofthemostimportantprobabilisticlogiclearningformalisms and systems. We are very pleased and proud that the scientists behind the key probabilistic inductive logic programming systems (also those developed outside the APRIL project) have kindly contributed a chapter providing an overviewoftheircontributions. Thisincludes: relationalsequencelearningte- niques (Kersting et al. ), using kernels with logical representations (Frasconi andPasserini), MarkovLogic(Domingosetal. ), the PRISMsystem (Satoand Kameya), CLP(BN)(SantosCostaetal. ), BayesianLogicPrograms(Kersting andDeRaedt), andtheIndependentChoiceLogic(Poole). Thethirdpartthen provides a detailed account of some show-caseapplications of probabilistic - ductive logic programming, more specically: in protein fold discovery (Chen et al. ), haplotyping (Landwehr and Mielik ainen) and systems biology (Fages andSoliman). The nal parttouchesupon sometheoreticalinvestigationsand VI Preface includes chaptersonbehavioralcomparisonof probabilisticlogicprogramming representations(MuggletonandChen)andamodel-theoreticexpressivityan- ysis(Jaeger). This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Bookseller Inventory # 9783540786511

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Book Description Springer, 2017. Paperback. Book Condition: New. Never used! This item is printed on demand. Bookseller Inventory # P113540786511

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Luc De Raedt
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Book Description Springer Mrz 2008, 2008. Taschenbuch. Book Condition: Neu. Neuware - One of the key open questions within arti cial intelligence is how to combine probability and logic with learning. This question is getting an increased - tentioninseveraldisciplinessuchasknowledgerepresentation,reasoningabout uncertainty, data mining, and machine learning simulateously, resulting in the newlyemergingsub eldknownasstatisticalrelationallearningandprobabil- ticinductivelogicprogramming.Amajordriving forceisthe explosivegrowth in the amount of heterogeneous data that is being collected in the business and scienti c world. Example domains include bioinformatics, chemoinform- ics, transportation systems, communication networks, social network analysis, linkanalysis,robotics,amongothers.Thestructuresencounteredcanbeass- pleassequencesandtrees(suchasthosearisinginproteinsecondarystructure predictionandnaturallanguageparsing)orascomplexascitationgraphs,the WorldWideWeb,andrelationaldatabases. This book providesan introduction to this eld with an emphasison those methods based on logic programming principles. The book is also the main resultofthesuccessfulEuropeanISTFETprojectno.FP6-508861onAppli- tionofProbabilisticInductiveLogicProgramming(APRILII,2004-2007).Thi s projectwascoordinatedbytheAlbertLudwigsUniversityofFreiburg(Germany, Luc De Raedt) and the partners were Imperial College London (UK, Stephen MuggletonandMichaelSternberg),theHelsinkiInstituteofInformationTe- nology(Finland,HeikkiMannila),theUniversit` adegliStudidiFlorence(Italy, PaoloFrasconi),andtheInstitutNationaldeRechercheenInformatiqueet- tomatiqueRocquencourt(France,FrancoisFages).Itwasconcernedwiththeory, implementationsandapplicationsofprobabilisticinductivelogicp rogramming. Thisstructureisalsore ectedinthebook. The book starts with an introductory chapter to Probabilistic Inductive LogicProgramming byDeRaedtandKersting.Inasecondpart,itprovidesa detailedoverviewofthemostimportantprobabilisticlogiclearningformalisms and systems. We are very pleased and proud that the scientists behind the key probabilistic inductive logic programming systems (also those developed outside the APRIL project) have kindly contributed a chapter providing an overviewoftheircontributions.Thisincludes:relationalsequencelearningte- niques (Kersting et al.), using kernels with logical representations (Frasconi andPasserini),MarkovLogic(Domingosetal.), the PRISMsystem (Satoand Kameya),CLP(BN)(SantosCostaetal.),BayesianLogicPrograms(Kersting andDeRaedt),andtheIndependentChoiceLogic(Poole).Thethirdpartthen provides a detailed account of some show-caseapplications of probabilistic - ductive logic programming, more speci cally: in protein fold discovery (Chen et al.), haplotyping (Landwehr and Mielik ainen) and systems biology (Fages andSoliman). The nal parttouchesupon sometheoreticalinvestigationsand VI Preface includes chaptersonbehavioralcomparisonof probabilisticlogicprogramming representations(MuggletonandChen)andamodel-theoreticexpressivityan- ysis(Jaeger). 356 pp. Englisch. Bookseller Inventory # 9783540786511

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