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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 Affordance Learning for Task-Dependent Robot Grasping
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    Chapter 2 Positive and Unlabeled Relational Classification Through Label Frequency Estimation
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    Chapter 3 On Applying Probabilistic Logic Programming to Breast Cancer Data
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    Chapter 4 Logical Vision: One-Shot Meta-Interpretive Learning from Real Images
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    Chapter 5 Demystifying Relational Latent Representations
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    Chapter 6 Parallel Online Learning of Event Definitions
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    Chapter 7 Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach
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    Chapter 8 Parallel Inductive Logic Programming System for Superlinear Speedup
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    Chapter 9 Inductive Learning from State Transitions over Continuous Domains
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    Chapter 10 Stacked Structure Learning for Lifted Relational Neural Networks
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    Chapter 11 Pruning Hypothesis Spaces Using Learned Domain Theories
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    Chapter 12 An Investigation into the Role of Domain-Knowledge on the Use of Embeddings
Attention for Chapter 2: Positive and Unlabeled Relational Classification Through Label Frequency Estimation
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Citations

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Readers on

mendeley
11 Mendeley
Summary Dimensions citations
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Chapter title
Positive and Unlabeled Relational Classification Through Label Frequency Estimation
Chapter number 2
Book title
Inductive Logic Programming
Published by
Springer, Cham, September 2017
DOI 10.1007/978-3-319-78090-0_2
Book ISBNs
978-3-31-978089-4, 978-3-31-978090-0
Authors

Jessa Bekker, Jesse Davis, Bekker, Jessa, Davis, Jesse

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 27%
Student > Master 2 18%
Researcher 2 18%
Student > Doctoral Student 1 9%
Professor > Associate Professor 1 9%
Other 0 0%
Unknown 2 18%
Readers by discipline Count As %
Computer Science 9 82%
Unknown 2 18%

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