Probabilistic Inductive Logic Programming (12 results)
- Softcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
Contact seller5-star sellerCondition: New
US$ 70.54
US$ 16.15 shippingShips from United Kingdom to U.S.A.Quantity: Over 20 available
Condition: New. In.
- Softcover
Seller: Chiron Media, Wallingford, United KingdomChiron Media
Contact seller5-star sellerCondition: New
US$ 66.34
US$ 20.89 shippingShips from United Kingdom to U.S.A.Quantity: 10 available
Paperback. Condition: New.
- Softcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
Contact seller4-star sellerCondition: New
US$ 90.11
US$ 3.99 shippingShips within U.S.A.Quantity: 4 available
Condition: New. pp. 356.
- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
Contact seller5-star sellerCondition: New
US$ 93.06
US$ 16.85 shippingShips from United Kingdom to U.S.A.Quantity: 2 available
Paperback. Condition: Brand New. 1st edition. 339 pages. 9.00x6.00x1.00 inches. In Stock.
- More images
- Softcover
Seller: preigu, Osnabrück, Germanypreigu
Contact seller5-star sellerCondition: New
US$ 59.87
US$ 80.74 shippingShips from Germany to U.S.A.Quantity: 5 available
Taschenbuch. Condition: Neu. Probabilistic Inductive Logic Programming | Luc De Raedt (u. a.) | Taschenbuch | viii | Englisch | 2008 | Springer | EAN 9783540786511 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
- More images
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
Contact seller5-star sellerCondition: New
US$ 71.24
US$ 72.32 shippingShips from Germany to U.S.A.Quantity: 1 available
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 min…ing, 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).This 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, implementationsandapplicationsofprobabilisticinductivelogicprogramming. 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).
- Softcover
- Print on Demand
Seller: Basi6 International, Irving, TX, U.S.A.Basi6 International
Contact seller5-star sellerCondition: New
US$ 68.36
Free ShippingShips within U.S.A.Quantity: 10 available
Condition: Brand New. New. US edition. Print on demand title. Delivery takes 20-25 days. Excellent Customer Service.
- More images
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
Contact seller5-star sellerCondition: New
US$ 63.54
US$ 26.53 shippingShips from Germany to U.S.A.Quantity: 2 available
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - 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 uncer…tainty, 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).This 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, implementationsandapplicationsofprobabilisticinductivelogicprogramming. 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.
- Softcover
- Print on Demand
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
Contact seller4-star sellerCondition: New
US$ 89.18
US$ 8.76 shippingShips from United Kingdom to U.S.A.Quantity: 4 available
Condition: New. Print on Demand pp. 356 Illus.
- Softcover
- Print on Demand
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
Contact seller4-star sellerCondition: New
US$ 96.56
US$ 11.48 shippingShips from Germany to U.S.A.Quantity: 4 available
Condition: New. PRINT ON DEMAND pp. 356.
- More images
- Softcover
- Print on Demand
Seller: moluna, Greven, Germanymoluna
Contact seller5-star sellerCondition: New
US$ 57.46
US$ 56.50 shippingShips from Germany to U.S.A.Quantity: Over 20 available
Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Probabilistic Inductive Logic Programming.- Formalisms and Systems.- Relational Sequence Learning.- Learning with Kernels and Logical Representations.- Markov Logic.- New Advances in Logic-Bas…ed Probabilistic Modeling by PRISM.- CLP( ): Constraint Logic .
- More images
- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
Contact seller5-star sellerCondition: New
US$ 63.54
US$ 69.20 shippingShips from Germany to U.S.A.Quantity: 1 available
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 356 pp. Englisch.
