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Inductive Logic Programming

Overview of attention for book
Cover of 'Inductive Logic Programming'

Table of Contents

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    Book Overview
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    Chapter 1 Reframing on Relational Data
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    Chapter 2 Inductive Learning Using Constraint-Driven Bias
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    Chapter 3 Nonmonotonic Learning in Large Biological Networks
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    Chapter 4 Construction of Complex Aggregates with Random Restart Hill-Climbing
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    Chapter 5 Logical Minimisation of Meta-Rules Within Meta-Interpretive Learning
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    Chapter 6 Goal and Plan Recognition via Parse Trees Using Prefix and Infix Probability Computation
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    Chapter 7 Effectively Creating Weakly Labeled Training Examples via Approximate Domain Knowledge
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    Chapter 8 Learning Prime Implicant Conditions from Interpretation Transition
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    Chapter 9 Statistical Relational Learning for Handwriting Recognition
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    Chapter 10 The Most Probable Explanation for Probabilistic Logic Programs with Annotated Disjunctions
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    Chapter 11 Towards Machine Learning of Predictive Models from Ecological Data
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    Chapter 12 PageRank, ProPPR, and Stochastic Logic Programs
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    Chapter 13 Complex Aggregates over Clusters of Elements
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    Chapter 14 On the Complexity of Frequent Subtree Mining in Very Simple Structures
Attention for Chapter 4: Construction of Complex Aggregates with Random Restart Hill-Climbing
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Chapter title
Construction of Complex Aggregates with Random Restart Hill-Climbing
Chapter number 4
Book title
Inductive Logic Programming
Published by
Springer, Cham, January 2015
DOI 10.1007/978-3-319-23708-4_4
Book ISBNs
978-3-31-923707-7, 978-3-31-923708-4
Authors

Clément Charnay, Nicolas Lachiche, Agnès Braud, Charnay, Clément, Lachiche, Nicolas, Braud, Agnès

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 100%
Readers by discipline Count As %
Computer Science 1 100%

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