LEADER 08892nam 22007455 450 001 996466006103316 005 20200703133616.0 010 $a3-319-40566-7 024 7 $a10.1007/978-3-319-40566-7 035 $a(CKB)3710000000734812 035 $a(DE-He213)978-3-319-40566-7 035 $a(MiAaPQ)EBC6301345 035 $a(MiAaPQ)EBC5596490 035 $a(Au-PeEL)EBL5596490 035 $a(OCoLC)952577776 035 $a(PPN)194378500 035 $a(EXLCZ)993710000000734812 100 $a20160609d2016 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aInductive Logic Programming$b[electronic resource] $e25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers /$fedited by Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (X, 215 p. 56 illus.) 225 1 $aLecture Notes in Artificial Intelligence ;$v9575 311 $a3-319-40565-9 327 $aIntro -- Preface -- Organization -- Contents -- Relational Kernel-Based Grasping with Numerical Features -- 1 Introduction -- 2 The Robot Grasping Scenario and Grasping Primitives -- 3 Relational Grasping: Problem Formulation -- 3.1 Data Modeling -- 3.2 Declarative and Relational Feature Construction -- 3.3 The Relational Problem Definition -- 3.4 Graphicalization -- 4 Relational Kernel Features -- 5 Experiments -- 5.1 Dataset and Evaluation -- 5.2 Results and Discussion -- 6 Related Work -- 7 Conclusions -- References -- CARAF: Complex Aggregates within Random Forests -- 1 Introduction and Context -- 2 Complex Aggregates -- 3 Random Forests -- 4 CARAF: Complex Aggregates with RAndom Forests -- 5 Experimental Results -- 6 Aggregation Processes Selection with Random Forests -- 7 Conclusion and Future Work -- References -- Distributed Parameter Learning for Probabilistic Ontologies -- 1 Introduction -- 2 Description Logics -- 3 Semantics and Reasoning in Probabilistic DLs -- 4 Parameter Learning for Probabilistic DLs -- 5 Distributed Parameter Learning for Probabilistic DLs -- 5.1 Architecture -- 5.2 MapReduce View -- 5.3 Scheduling Techniques -- 5.4 Overall EDGEMR -- 6 Experiments -- 7 Related Work -- 8 Conclusions -- References -- Meta-Interpretive Learning of Data Transformation Programs -- 1 Introduction -- 2 Related Work -- 3 Framework -- 4 Implementation -- 4.1 Transformation Language -- 5 Experiments -- 5.1 XML Data Transformations -- 5.2 Ecological Scholarly Papers -- 5.3 Patient Medical Records -- 6 Conclusion and Further Work -- A Appendix 1 -- B Appendix 2 -- References -- Statistical Relational Learning with Soft Quantifiers -- 1 Introduction -- 2 PSLQ: PSL with Soft Quantifiers -- 3 Inference and Weight Learning in PSLQ -- 3.1 Inference -- 3.2 Weight Learning -- 4 Evaluation: Trust Link Prediction -- 5 Conclusion -- References. 327 $aOntology Learning from Interpretations in Lightweight Description Logics -- 1 Introduction -- 2 Description Logic Preliminaries -- 3 Learning Model -- 4 Finite Learning Sets -- 5 Learning Algorithms -- 6 Related Work -- 7 Conclusions and Outlook -- References -- Constructing Markov Logic Networks from First-Order Default Rules -- 1 Introduction -- 2 Background -- 2.1 Markov Logic Networks -- 2.2 Reasoning About Default Rules in System P -- 3 Encoding Ground Default Theories in Markov Logic -- 4 Encoding Non-ground Default Theories in Markov Logic -- 5 Evaluation -- 6 Conclusion -- A Proofs -- References -- Mine 'Em All: A Note on Mining All Graphs -- 1 Introduction -- 2 Preliminaries -- 3 Graph Mining Problems -- 4 Mining All (Induced) Subgraphs -- 4.1 Negative Results -- 4.2 Positive Results for ALLF I and ALLS L -- 4.3 Positive Results for ALLL S -- 4.4 Other Negative Results -- 5 Mining Under Homeomorphism and Minor Embedding -- 6 Conclusions and Future Work -- References -- Processing Markov Logic Networks with GPUs: Accelerating Network Grounding -- 1 Introduction -- 2 Markov Logic, Tuffy, Datalog and GPUs -- 2.1 Inference in Markov Logic -- 2.2 Optimizations -- 2.3 Learning -- 2.4 Tuffy -- 2.5 Evaluation of Datalog Programs -- 2.6 GPU Architecture and Programming -- 3 Our GPU-Based Markov Logic Platform -- 4 Experimental Evaluation -- 4.1 Applications and Hardware-Software Platform -- 4.2 Results -- 5 Related Work -- 6 Conclusions -- References -- Using ILP to Identify Pathway Activation Patterns in Systems Biology -- 1 Introduction and Background -- 2 Overview of Propositionalization -- 3 Methods -- 3.1 Raw Data -- 3.2 Data Processing -- 3.3 Searching for Pathway Activation Patterns -- 4 Results -- 4.1 Quantitative Evaluation and Comparison with SBV Improver Model -- 4.2 Results for Warmr Method. 327 $a4.3 Results for Warmr/TreeLiker Combined Method -- 5 Conclusions -- References -- kProbLog: An Algebraic Prolog for Kernel Programming -- 1 Introduction -- 2 KProbLogS -- 3 kProbLog -- 3.1 Recursive kProbLog Program with Meta-Functions -- 3.2 The Jacobi Method -- 3.3 kProbLog TP-Operator with Meta-Functions -- 4 kProbLogS[x] -- 4.1 Polynomials for Feature Extraction -- 4.2 The @id Meta-Function -- 5 Graph Kernels -- 5.1 Weisfeiler-Lehman Graph Kernel and Propagation Kernels -- 5.2 Graph Invariant Kernels -- 6 Conclusions -- References -- An Exercise in Declarative Modeling for Relational Query Mining -- 1 Introduction -- 2 Problem Statement -- 3 Encoding -- 4 First Order Model -- 5 Experiments -- 6 Model Discussion and Generalization -- 7 Related Work -- 8 Conclusions -- A Appendix: Introduction to IDP -- References -- Learning Inference by Induction -- 1 Introduction -- 2 Learning Logical Inference -- 2.1 Learning Logics -- 2.2 Learning from 1-Step Transitions -- 2.3 Learning Deduction Rules by LF1T -- 3 Learning Non-logical Inference Rules -- 3.1 Abduction -- 3.2 Frame Axiom -- 3.3 Conversational Implicature -- 4 Discussion -- 5 Conclusion -- References -- Identification of Transition Models of Biological Systems in the Presence of Transition Noise -- 1 Introduction -- 2 Transition Identification Under Transition Noise -- 3 Empirical Evaluation -- 3.1 Problems -- 3.2 Data -- 3.3 Models -- 3.4 Algorithms and Machines -- 3.5 Method -- 3.6 Results -- 3.7 Transition Identification Worked Example: Water -- 4 Related Work -- 5 Conclusion -- References -- Author Index. 330 $aThis book constitutes the thoroughly refereed post-conference proceedings of the 25th International Conference on Inductive Logic Programming, ILP 2015, held in Kyoto, Japan, in August 2015. The 14 revised papers presented were carefully reviewed and selected from 44 submissions. The papers focus on topics such as theories, algorithms, representations and languages, systems and applications of ILP, and cover all areas of learning in logic, relational learning, relational data mining, statistical relational learning, multi-relational data mining, relational reinforcement learning, graph mining, connections with other learning paradigms, among others. 410 0$aLecture Notes in Artificial Intelligence ;$v9575 606 $aMathematical logic 606 $aArtificial intelligence 606 $aComputer programming 606 $aComputer logic 606 $aData mining 606 $aMathematical Logic and Formal Languages$3https://scigraph.springernature.com/ontologies/product-market-codes/I16048 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 $aLogics and Meanings of Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/I1603X 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 615 0$aMathematical logic. 615 0$aArtificial intelligence. 615 0$aComputer programming. 615 0$aComputer logic. 615 0$aData mining. 615 14$aMathematical Logic and Formal Languages. 615 24$aArtificial Intelligence. 615 24$aProgramming Techniques. 615 24$aLogics and Meanings of Programs. 615 24$aData Mining and Knowledge Discovery. 676 $a005.115 702 $aInoue$b Katsumi$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aOhwada$b Hayato$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aYamamoto$b Akihiro$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466006103316 996 $aInductive Logic Programming$92804417 997 $aUNISA LEADER 03542nam 22007455 450 001 9910298655503321 005 20200701150604.0 010 $a3-319-06170-4 024 7 $a10.1007/978-3-319-06170-2 035 $a(CKB)3710000000111954 035 $a(EBL)1731104 035 $a(OCoLC)885122208 035 $a(SSID)ssj0001237428 035 $a(PQKBManifestationID)11951018 035 $a(PQKBTitleCode)TC0001237428 035 $a(PQKBWorkID)11259018 035 $a(PQKB)10981445 035 $a(MiAaPQ)EBC1731104 035 $a(DE-He213)978-3-319-06170-2 035 $a(PPN)178785350 035 $a(EXLCZ)993710000000111954 100 $a20140508d2014 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aDynamic Studies Through Control of Relaxation in NMR Spectroscopy /$fby Nicola Salvi 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (118 p.) 225 1 $aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053 300 $a"Doctoral Thesis accepted by Ecole Polytechnique Fe?de?rale de Lausanne, Switzerland." 311 $a3-319-06169-0 320 $aIncludes bibliographical references at the end of each chapters. 327 $aIntroduction -- Theoretical principles -- Analytical models for relaxation dispersion experiments -- Experimental methods -- Experimental results -- Conclusions. 330 $aNicola Salvi's thesis offers a remarkably cogent view of highly sophisticated NMR methods. Salvi developed these methods in order to characterize the amplitudes and frequency ranges of local motions in biomolecules such as proteins. These local motions play an essential role since they can explain many of the remarkable properties of proteins and enable them to carry out all sorts of vital functions, from enzymatic catalysis to intermolecular recognition and signalling in cells. 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