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Inductive Logic Programming : 22nd International Conference, ILP 2012, Dubrovnik, Croatia, September 16-18,2012, Revised Selected papers / edited by Fabrizio Riguzzi, Filip Zelezny.

SpringerLink Books Computer Science (2011-2024) Available online

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Format:
Book
Contributor:
Riguzzi, Fabrizio, Editor.
Železný, Filip, Editor.
SpringerLink (Online service)
Series:
Computer Science (SpringerNature-11645)
Lecture notes in computer science. Lecture notes in artificial intelligence 2945-9141 ; 7842
Lecture Notes in Artificial Intelligence, 2945-9141 ; 7842
Language:
English
Subjects (All):
Machine theory.
Artificial intelligence.
Computer programming.
Computer science.
Formal Languages and Automata Theory.
Artificial Intelligence.
Programming Techniques.
Computer Science Logic and Foundations of Programming.
Theory of Computation.
Computer Science.
Local Subjects:
Formal Languages and Automata Theory.
Artificial Intelligence.
Programming Techniques.
Computer Science Logic and Foundations of Programming.
Theory of Computation.
Computer Science.
Physical Description:
1 online resource (X, 273 pages) : 81 illustrations
Edition:
1st ed. 2013.
Contained In:
Springer Nature eBook
Place of Publication:
Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
System Details:
text file PDF
Summary:
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.
Contents:
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
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.
Other Format:
Printed edition:
ISBN:
978-3-642-38812-5
9783642388125
Access Restriction:
Restricted for use by site license.

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