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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 45 Inductive logic programming for natural language processing
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    Chapter 46 An initial experiment into stereochemistry-based drug design using inductive logic programming
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    Chapter 47 Applying ILP to diterpene structure elucidation from 13 C NMR spectra
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    Chapter 48 Analysis and prediction of piano performances using inductive logic programming
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    Chapter 49 Noise detection and elimination applied to noise handling in a KRK chess endgame
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    Chapter 50 Inductive Logic Programming
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    Chapter 51 Polynomial-time learning in logic programming and constraint logic programming
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    Chapter 52 Analyzing and learning ECG waveforms
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    Chapter 53 Learning rules that classify ocular fundus images for glaucoma diagnosis
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    Chapter 54 A new design and implementation of progol by bottom-up computation
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    Chapter 55 Inductive logic program synthesis with DIALOGS
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    Chapter 56 Relational knowledge discovery in databases
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    Chapter 57 Efficient θ-subsumption based on graph algorithms
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    Chapter 58 Integrity constraints in ILP using a Monte Carlo approach
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    Chapter 59 Restructuring chain datalog programs
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    Chapter 60 Top-down induction of logic programs from incomplete samples
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    Chapter 61 Least generalizations under implication
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    Chapter 62 Efficient proof encoding
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    Chapter 63 Learning Logic programs with random classification noise
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    Chapter 64 Handling Quantifiers in ILP
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    Chapter 65 Learning from positive data
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    Chapter 66 λ-Subsumption and its application to learning from positive-only examples
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2 Wikipedia pages


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Inductive Logic Programming
Published by
Springer Berlin Heidelberg, April 2006
DOI 10.1007/3-540-63494-0
978-3-54-063494-2, 978-3-54-069583-7

Muggleton, Stephen

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Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 1 50%
Unknown 1 50%
Readers by discipline Count As %
Computer Science 1 50%
Unknown 1 50%