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Inductive Logic Programming : 21st International Conference, ILP 2011, Windsor Great Park, UK, July 31 August 3, 2011, Revised Selected Papers
This book constitutes the thoroughly refereed post-proceedings of the 21st International Conference on Inductive Logic Programming, ILP 2011, held in Windsor Great Park, UK, in July/August 2011. The 24 revised full papers were carefully reviewed and selected from 66 submissions. Also included are fi...
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| Auteurs principaux : | , , , |
|---|---|
| Format : | Livre |
| Langue : | anglais |
| Titre complet : | Inductive Logic Programming : 21st International Conference, ILP 2011, Windsor Great Park, UK, July 31 August 3, 2011, Revised Selected Papers / edited by Stephen H. Muggleton, Alireza Tamaddoni-Nezhad, Francesca A. Lisi. |
| Édition : | 1st ed. 2012. |
| Publié : |
Berlin, Heidelberg :
Springer Berlin Heidelberg
, 2012 |
| Collection : | Lecture Notes in Artificial Intelligence ; 7207 |
| Accès en ligne : |
Accès Nantes Université
Accès direct soit depuis les campus via le réseau ou le wifi eduroam soit à distance avec un compte @etu.univ-nantes.fr ou @univ-nantes.fr |
| Contenu : | Inference and Learning. Beyond Reward: The Problem of Knowledge and Data. Exploiting Constraints. Online Bayesian Inference for the Parameters of PRISM Programs. Learning Compact Markov Logic Networks with Decision Trees. Relational Networks of Conditional Preferences. k-Optimal: A Novel Approximate Inference Algorithm for ProbLog. Learning Directed Relational Models with Recursive Dependencies. Integrating Model Checking and Inductive Logic Programming. Inductive Logic Programming in Answer Set Programming. Graph-Based Relational Learning with a Polynomial Time Projection Algorithm. Interleaved Inductive-Abductive Reasoning for Learning Complex Event Models. Conceptual Clustering of Multi-Relational Data. Expressive Power of Safe First-Order Logical Decision Trees. Relational Learning for Spatial Relation Extraction from Natural |
| Sujets : | |
| Documents associés : | Autre format:
Inductive Logic Programming Autre format: Inductive Logic Programming |
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| 200 | 1 | |a Inductive Logic Programming |e 21st International Conference, ILP 2011, Windsor Great Park, UK, July 31 August 3, 2011, Revised Selected Papers |f edited by Stephen H. Muggleton, Alireza Tamaddoni-Nezhad, Francesca A. Lisi. | |
| 205 | |a 1st ed. 2012. | ||
| 214 | 0 | |a Berlin, Heidelberg |c Springer Berlin Heidelberg |d 2012 | |
| 225 | 0 | |a Lecture Notes in Artificial Intelligence |x 2945-9141 |v 7207 | |
| 230 | |a Données textuelles | ||
| 327 | 1 | |a Inference and Learning |a Beyond Reward: The Problem of Knowledge and Data |a Exploiting Constraints |a Online Bayesian Inference for the Parameters of PRISM Programs |a Learning Compact Markov Logic Networks with Decision Trees |a Relational Networks of Conditional Preferences |a k-Optimal: A Novel Approximate Inference Algorithm for ProbLog |a Learning Directed Relational Models with Recursive Dependencies |a Integrating Model Checking and Inductive Logic Programming |a Inductive Logic Programming in Answer Set Programming |a Graph-Based Relational Learning with a Polynomial Time Projection Algorithm |a Interleaved Inductive-Abductive Reasoning for Learning Complex Event Models |a Conceptual Clustering of Multi-Relational Data |a Expressive Power of Safe First-Order Logical Decision Trees |a Relational Learning for Spatial Relation Extraction from Natural | |
| 330 | |a This book constitutes the thoroughly refereed post-proceedings of the 21st International Conference on Inductive Logic Programming, ILP 2011, held in Windsor Great Park, UK, in July/August 2011. The 24 revised full papers were carefully reviewed and selected from 66 submissions. Also included are five extended abstracts and three invited talks. The papers represent the diversity and vitality in present ILP research including ILP theory, implementations, probabilistic ILP, biological applications, sub-group discovery, grammatical inference, relational kernels, learning of Petri nets, spatial learning, graph-based learning, and learning of action models | ||
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