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Inductive Logic Programming

Overview of attention for book
Inductive Logic Programming
Springer Berlin Heidelberg

Table of Contents

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    Book Overview
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    Chapter 1 Inductive Logic Programming
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    Chapter 2 A Refinement Operator for Inducing Threaded-Variable Clauses
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    Chapter 3 Propositionalisation of Continuous Attributes beyond Simple Aggregation
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    Chapter 4 Topic Models with Relational Features for Drug Design
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    Chapter 5 Pairwise Markov Logic
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    Chapter 6 Evaluating Inference Algorithms for the Prolog Factor Language
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    Chapter 7 Polynomial Time Pattern Matching Algorithm for Ordered Graph Patterns
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    Chapter 8 Fast Parameter Learning for Markov Logic Networks Using Bayes Nets
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    Chapter 9 Bounded Least General Generalization
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    Chapter 10 Itemset-Based Variable Construction in Multi-relational Supervised Learning
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    Chapter 11 A Declarative Modeling Language for Concept Learning in Description Logics
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    Chapter 12 Identifying Driver’s Cognitive Load Using Inductive Logic Programming
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    Chapter 13 Opening Doors: An Initial SRL Approach
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    Chapter 14 Probing the Space of Optimal Markov Logic Networks for Sequence Labeling
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    Chapter 15 What Kinds of Relational Features Are Useful for Statistical Learning?
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    Chapter 16 Learning Dishonesty
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    Chapter 17 Heuristic Inverse Subsumption in Full-Clausal Theories
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    Chapter 18 Learning Unordered Tree Contraction Patterns in Polynomial Time
Attention for Chapter 17: Heuristic Inverse Subsumption in Full-Clausal Theories
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Chapter title
Heuristic Inverse Subsumption in Full-Clausal Theories
Chapter number 17
Book title
Inductive Logic Programming
Published by
Springer, Berlin, Heidelberg, September 2012
DOI 10.1007/978-3-642-38812-5_17
Book ISBNs
978-3-64-238811-8, 978-3-64-238812-5
Authors

Yoshitaka Yamamoto, Katsumi Inoue, Koji Iwanuma, Yamamoto, Yoshitaka, Inoue, Katsumi, Iwanuma, Koji

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