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Inductive Logic Programming

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Cover of 'Inductive Logic Programming'

Table of Contents

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    Book Overview
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    Chapter 1 Estimation-Based Search Space Traversal in PILP Environments
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    Chapter 2 Inductive Logic Programming Meets Relational Databases: Efficient Learning of Markov Logic Networks
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    Chapter 3 Online Structure Learning for Traffic Management
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    Chapter 4 Learning Through Advice-Seeking via Transfer
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    Chapter 5 How Does Predicate Invention Affect Human Comprehensibility?
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    Chapter 6 Distributional Learning of Regular Formal Graph System of Bounded Degree
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    Chapter 7 Learning Relational Dependency Networks for Relation Extraction
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    Chapter 8 Towards Nonmonotonic Relational Learning from Knowledge Graphs
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    Chapter 9 Learning Predictive Categories Using Lifted Relational Neural Networks
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    Chapter 10 Generation of Near-Optimal Solutions Using ILP-Guided Sampling
Attention for Chapter 10: Generation of Near-Optimal Solutions Using ILP-Guided Sampling
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Chapter title
Generation of Near-Optimal Solutions Using ILP-Guided Sampling
Chapter number 10
Book title
Inductive Logic Programming
Published by
Springer, Cham, September 2016
DOI 10.1007/978-3-319-63342-8_10
Book ISBNs
978-3-31-963341-1, 978-3-31-963342-8
Authors

Ashwin Srinivasan, Gautam Shroff, Lovekesh Vig, Sarmimala Saikia, Srinivasan, Ashwin, Shroff, Gautam, Vig, Lovekesh, Saikia, Sarmimala

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