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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 Relational Kernel-Based Grasping with Numerical Features
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    Chapter 2 CARAF: Complex Aggregates within Random Forests
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    Chapter 3 Distributed Parameter Learning for Probabilistic Ontologies
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    Chapter 4 Meta-Interpretive Learning of Data Transformation Programs
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    Chapter 5 Statistical Relational Learning with Soft Quantifiers
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    Chapter 6 Ontology Learning from Interpretations in Lightweight Description Logics
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    Chapter 7 Constructing Markov Logic Networks from First-Order Default Rules
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    Chapter 8 Mine ’Em All: A Note on Mining All Graphs
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    Chapter 9 Processing Markov Logic Networks with GPUs: Accelerating Network Grounding
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    Chapter 10 Using ILP to Identify Pathway Activation Patterns in Systems Biology
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    Chapter 11 kProbLog: An Algebraic Prolog for Kernel Programming
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    Chapter 12 An Exercise in Declarative Modeling for Relational Query Mining
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    Chapter 13 Learning Inference by Induction
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    Chapter 14 Identification of Transition Models of Biological Systems in the Presence of Transition Noise
Attention for Chapter 7: Constructing Markov Logic Networks from First-Order Default Rules
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Citations

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7 Mendeley
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Chapter title
Constructing Markov Logic Networks from First-Order Default Rules
Chapter number 7
Book title
Inductive Logic Programming
Published by
Springer, Cham, August 2015
DOI 10.1007/978-3-319-40566-7_7
Book ISBNs
978-3-31-940565-0, 978-3-31-940566-7
Authors

Ondřej Kuželka, Jesse Davis, Steven Schockaert

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 29%
Student > Ph. D. Student 1 14%
Lecturer > Senior Lecturer 1 14%
Student > Master 1 14%
Unknown 2 29%
Readers by discipline Count As %
Computer Science 5 71%
Unknown 2 29%

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