Description |
1 online resource (x, 215 pages) : illustrations |
Series |
Lecture notes in artificial intelligence ; 9575 |
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LNCS sublibrary. SL 7, Artificial intelligence |
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Lecture notes in computer science. Lecture notes in artificial intelligence ; 9575
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LNCS sublibrary. SL 7, Artificial intelligence.
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Contents |
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 |
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3 Random Forests4 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 |
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Meta-Interpretive Learning of Data Transformation Programs1 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 |
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Ontology Learning from Interpretations in Lightweight Description Logics1 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 |
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Mine 'Em All: A Note on Mining All Graphs1 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 |
Summary |
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 |
Bibliography |
Includes bibliographical references and author index |
Notes |
Online resource; title from PDF title page (SpringerLink, viewed June 30, 2016) |
Subject |
Logic programming -- Congresses
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Induction (Logic) -- Congresses
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Machine learning -- Congresses
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Artificial intelligence.
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Computer programming -- software development.
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Data mining.
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Mathematical theory of computation.
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Computers -- Intelligence (AI) & Semantics.
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Computers -- Programming -- General.
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Computers -- Database Management -- Data Mining.
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Mathematics -- Logic.
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Machine learning
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Logic programming
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Induction (Logic)
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Genre/Form |
proceedings (reports)
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Conference papers and proceedings
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Conference papers and proceedings.
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Actes de congrès.
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Form |
Electronic book
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Author |
Inoue, Katsumi, editor.
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Ohwada, Hayato, editor
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Yamamoto, Akihiro, 1960- editor.
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ISBN |
9783319405667 |
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3319405667 |
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