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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 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 14: Identification of Transition Models of Biological Systems in the Presence of Transition Noise
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