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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 A Refinement Operator for Theories
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    Chapter 2 Learning Logic Programs with Neural Networks
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    Chapter 3 A Genetic Algorithm for Propositionalization
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    Chapter 4 Classifying Uncovered Examples by Rule Stretching
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    Chapter 5 Relational Learning Using Constrained Confidence-Rated Boosting
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    Chapter 6 Induction, Abduction, and Consequence-Finding
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    Chapter 7 From Shell Logs to Shell Scripts
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    Chapter 8 Inductive Logic Programming
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    Chapter 9 Adaptive Bayesian Logic Programs
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    Chapter 10 Towards Combining Inductive Logic Programming with Bayesian Networks
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    Chapter 11 Demand-Driven Construction of Structural Features in ILP
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    Chapter 12 Transformation-Based Learning Using Multirelational Aggregation
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    Chapter 13 Discovering Associations between Spatial Objects: An ILP Application
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    Chapter 14 θ -Subsumption in a Constraint Satisfaction Perspective
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    Chapter 15 Learning to Parse from a Treebank: Combining TBL and ILP
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    Chapter 16 Induction of Stable Models
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    Chapter 17 Application of Pruning Techniques for Propositional Learning to Progol
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    Chapter 18 Application of ILP to Cardiac Arrhythmia Characterization for Chronicle Recognition
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    Chapter 19 Efficient Cross-Validation in ILP
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    Chapter 20 Modelling Semi-structured Documents with Hedges for Deduction and Induction
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    Chapter 21 Learning Functions from Imperfect Positive Data
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Title
Inductive Logic Programming
Published by
Springer Berlin Heidelberg, June 2003
DOI 10.1007/3-540-44797-0
ISBNs
978-3-54-042538-0, 978-3-54-044797-9
Editors

Rouveirol, Céline, Sebag, Michéle

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Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 1 100%
Readers by discipline Count As %
Computer Science 1 100%