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[null,null,"Titolo",null,"Inductive Logic Programming [[electronic resource] ] : 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers / / edited by Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016"]
[null,null,"ISBN",null,"3-319-40566-7"]
[null,null,"Edizione",null,"[1st ed. 2016.]"]
[null,null,"Descrizione fisica",null,"1 online resource (X, 215 p. 56 illus.)"]
[null,null,"Collana",null,"Lecture Notes in Artificial Intelligence ; ; 9575"]
[null,null,"Disciplina",null,"005.115"]
[null,null,"Soggetti",null,"Mathematical logic"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computer programming"]
[{"$nulls":4},"Computer logic"]
[{"$nulls":4},"Data mining"]
[{"$nulls":4},"Mathematical Logic and Formal Languages"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Programming Techniques"]
[{"$nulls":4},"Logics and Meanings of Programs"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents -- Relational Kernel-Based Grasping with Numerical Features -- 1 Introduction -- 2 The Robot Grasping Scenario and Grasping Primitives -- 3 Relational Grasping: Problem Formulation -- 3.1 Data Modeling -- 3.2 Declarative and Relational Feature Construction -- 3.3 The Relational Problem Definition -- 3.4 Graphicalization -- 4 Relational Kernel Features -- 5 Experiments -- 5.1 Dataset and Evaluation -- 5.2 Results and Discussion -- 6 Related Work -- 7 Conclusions -- References -- CARAF: Complex Aggregates within Random Forests -- 1 Introduction and Context -- 2 Complex Aggregates -- 3 Random Forests -- 4 CARAF: Complex Aggregates with RAndom Forests -- 5 Experimental Results -- 6 Aggregation Processes Selection with Random Forests -- 7 Conclusion and Future Work -- References -- Distributed Parameter "]
[{"$nulls":4},"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."]
[{"$nulls":4},"Ontology Learning from Interpretations in Lightweight Description Logics -- 1 Introduction -- 2 Description Logic Preliminaries -- 3 Learning Model -- 4 Finite Learning Sets -- 5 Learning Algorithms -- 6 Related Work -- 7 Conclusions and Outlook -- References -- Constructing Markov Logic Networks from First-Order Default Rules -- 1 Introduction -- 2 Background -- 2.1 Markov Logic Networks -- 2.2 Reasoning About Default Rules in System P -- 3 Encoding Ground Default Theories in Markov Logic -- 4 Encoding Non-ground Default Theories in Markov Logic -- 5 Evaluation -- 6 Conclusion -- A Proofs -- References -- Mine 'Em All: A Note on Mining All Graphs -- 1 Introduction -- 2 Preliminaries -- 3 Graph Mining Problems -- 4 Mining All (Induced) Subgraphs -- 4.1 Negative Results -- 4.2 Positive Results for ALLF I and ALLS L -- 4.3 Positive Results for ALLL S -- 4.4 Other Negative Results -- 5 Mining Under Homeomorphism and Minor Embedding -- 6 Conclusions and Future Work -- References -- Processing Markov Logic Networks with GPUs: Accelerating Network Grounding -- 1 Introduction -- 2 Markov Logic, Tuffy, Datalog and GPUs -- 2.1 Inference in Markov Logic -- 2.2 Optimizations -- 2.3 Learning -- 2.4 Tuffy -- 2.5 Evaluation of Datalog Programs -- 2.6 GPU Architecture and Programming -- 3 Our GPU-Based Markov Logic Platform -- 4 Experimental Evaluation -- 4.1 Applications and Hardware-Software Platform -- 4.2 Results -- 5 Related Work -- 6 Conclusions -- References -- Using ILP to Identify Pathway Activation Patterns in Systems Biology -- 1 Introduction and Background -- 2 Overview of Propositionalization -- 3 Methods -- 3.1 Raw Data -- 3.2 Data Processing -- 3.3 Searching for Pathway Activation Patterns -- 4 Results -- 4.1 Quantitative Evaluation and Comparison with SBV Improver Model -- 4.2 Results for Warmr Method."]
[{"$nulls":4},"4.3 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 "]
[{"$nulls":4},"-- 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."]
[null,null,null,"Sommario/riassunto","This 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."]