Inductive Logic Programming

Table of Contents

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   Book Overview
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   Chapter 1 Automated Synthesis of Data Analysis Programs: Learning in Logic
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   Chapter 2 At the Interface of Inductive Logic Programming and Statistics
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   Chapter 3 From Promising to Profitable Applications of ILP: A Case Study in Drug Discovery
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   Chapter 4 Systems Biology: A New Challenge for ILP
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   Chapter 5 Scaling Up ILP: Experiences with Extracting Relations from Biomedical Text
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   Chapter 6 Macro-Operators Revisited in Inductive Logic Programming
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   Chapter 7 Bottom-Up ILP Using Large Refinement Steps
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   Chapter 8 On the Effect of Caching in Recursive Theory Learning
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    Chapter 9 FOIL-D: Efficiently Scaling FOIL for Multi-relational Data Mining of Large Datasets
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    Chapter 10 Learning an Approximation to Inductive Logic Programming Clause Evaluation
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    Chapter 11 Learning Ensembles of First-Order Clauses for Recall-Precision Curves: A Case Study in Biomedical Information Extraction
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    Chapter 12 Automatic Induction of First-Order Logic Descriptors Type Domains from Observations
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    Chapter 13 On Avoiding Redundancy in Inductive Logic Programming
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    Chapter 14 Generalization Algorithms for Second-Order Terms
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    Chapter 15 Circumscription Policies for Induction
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    Chapter 16 Logical Markov Decision Programs and the Convergence of Logical TD( λ )
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    Chapter 17 Learning Goal Hierarchies from Structured Observations and Expert Annotations
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    Chapter 18 Efficient Evaluation of Candidate Hypotheses in $\mathcal{AL}$ -log
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    Chapter 19 An Efficient Algorithm for Reducing Clauses Based on Constraint Satisfaction Techniques
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    Chapter 20 Improving Rule Evaluation Using Multitask Learning
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    Chapter 21 Learning Logic Programs with Annotated Disjunctions
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    Chapter 22 A Simulated Annealing Framework for ILP
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    Chapter 23 Modelling Inhibition in Metabolic Pathways Through Abduction and Induction
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    Chapter 24 First Order Random Forests with Complex Aggregates
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    Chapter 25 A Monte Carlo Study of Randomised Restarted Search in ILP
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    Chapter 26 Learning, Logic, and Probability: A Unified View