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Inductive Logic Programming 22nd International Conference, ILP 2012, Dubrovnik, Croatia, September 16-18,2012, Revised Selected papers /
This book constitutes the thoroughly refereed post-proceedings of the 22nd International Conference on Inductive Logic Programming, ILP 2012, held in Dubrovnik, Croatia, in September 2012. The 18 revised full papers were carefully reviewed and selected from 41 submissions. The papers cover the follo...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2013.
|
| Edición: | 1st ed. 2013. |
| Colección: | Lecture Notes in Artificial Intelligence,
7842 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 245 | 1 | 0 | |a Inductive Logic Programming |h [electronic resource] : |b 22nd International Conference, ILP 2012, Dubrovnik, Croatia, September 16-18,2012, Revised Selected papers / |c edited by Fabrizio Riguzzi, Filip Zelezny. |
| 250 | |a 1st ed. 2013. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2013. | |
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| 490 | 1 | |a Lecture Notes in Artificial Intelligence, |x 2945-9141 ; |v 7842 | |
| 505 | 0 | |a A Relational Approach to Tool-Use Learning in Robots -- A Refinement Operator for Inducing Threaded-Variable Clauses -- Propositionalisation of Continuous Attributes beyond Simple Aggregation -- Topic Models with Relational Features for Drug Design -- Pairwise Markov Logic -- Evaluating Inference Algorithms for the Prolog Factor Language -- Polynomial Time Pattern Matching Algorithm for Ordered Graph Patterns -- Fast Parameter Learning for Markov Logic Networks Using Bayes Nets -- Bounded Least General Generalization -- Itemset-Based Variable Construction in Multi-relational Supervised Learning -- A Declarative Modeling Language for Concept Learning in Description Logics -- Identifying Driver's Cognitive Load Using Inductive Logic Programming -- Opening Doors: An Initial SRL Approach -- Probing the Space of Optimal Markov Logic Networks for Sequence Labeling -- What Kinds of Relational Features Are Useful for Statistical Learning? -- Learning Dishonesty -- Heuristic Inverse Subsumption in Full-Clausal Theories -- Learning Unordered Tree Contraction Patterns in Polynomial TimeA Relational Approach to Tool-Use Learning in Robots -- A Refinement Operator for Inducing Threaded-Variable Clauses -- Propositionalisation of Continuous Attributes beyond Simple Aggregation -- Topic Models with Relational Features for Drug Design -- Pairwise Markov Logic -- Evaluating Inference Algorithms for the Prolog Factor Language -- Polynomial Time Pattern Matching Algorithm for Ordered Graph Patterns -- Fast Parameter Learning for Markov Logic Networks Using Bayes Nets -- Bounded Least General Generalization -- Itemset-Based Variable Construction in Multi-relational Supervised Learning -- A Declarative Modeling Language for Concept Learning in Description Logics -- Identifying Driver's Cognitive Load Using Inductive Logic Programming -- Opening Doors: An Initial SRL Approach -- Probing the Space of Optimal Markov Logic Networks for Sequence Labeling -- What Kinds of Relational Features Are Useful for Statistical Learning?.-Learning Dishonesty.-Heuristic Inverse Subsumption in Full-Clausal Theories.-Learning Unordered Tree Contraction Patterns in Polynomial Time. | |
| 520 | |a This book constitutes the thoroughly refereed post-proceedings of the 22nd International Conference on Inductive Logic Programming, ILP 2012, held in Dubrovnik, Croatia, in September 2012. The 18 revised full papers were carefully reviewed and selected from 41 submissions. The papers cover the following topics: propositionalization, logical foundations, implementations, probabilistic ILP, applications in robotics and biology, grammatical inference, spatial learning and graph-based learning. | ||
| 650 | 0 | |a Machine theory. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Computer programming. | |
| 650 | 0 | |a Computer science. | |
| 650 | 1 | 4 | |a Formal Languages and Automata Theory. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Programming Techniques. |
| 650 | 2 | 4 | |a Computer Science Logic and Foundations of Programming. |
| 650 | 2 | 4 | |a Theory of Computation. |
| 650 | 2 | 4 | |a Computer Science. |
| 700 | 1 | |a Riguzzi, Fabrizio. |e editor. |0 (orcid)0000-0003-1654-9703 |1 https://orcid.org/0000-0003-1654-9703 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Zelezny, Filip. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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| 776 | 0 | 8 | |i Printed edition: |z 9783642388118 |
| 830 | 0 | |a Lecture Notes in Artificial Intelligence, |x 2945-9141 ; |v 7842 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-642-38812-5 |z Texto Completo |
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| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||