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Inductive Logic Programming

Overview of attention for book
Cover of 'Inductive Logic Programming'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 45 Inductive logic programming for natural language processing
  3. Altmetric Badge
    Chapter 46 An initial experiment into stereochemistry-based drug design using inductive logic programming
  4. Altmetric Badge
    Chapter 47 Applying ILP to diterpene structure elucidation from 13 C NMR spectra
  5. Altmetric Badge
    Chapter 48 Analysis and prediction of piano performances using inductive logic programming
  6. Altmetric Badge
    Chapter 49 Noise detection and elimination applied to noise handling in a KRK chess endgame
  7. Altmetric Badge
    Chapter 50 Inductive Logic Programming
  8. Altmetric Badge
    Chapter 51 Polynomial-time learning in logic programming and constraint logic programming
  9. Altmetric Badge
    Chapter 52 Analyzing and learning ECG waveforms
  10. Altmetric Badge
    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
  12. Altmetric Badge
    Chapter 55 Inductive logic program synthesis with DIALOGS
  13. Altmetric Badge
    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
  16. Altmetric Badge
    Chapter 59 Restructuring chain datalog programs
  17. Altmetric Badge
    Chapter 60 Top-down induction of logic programs from incomplete samples
  18. Altmetric Badge
    Chapter 61 Least generalizations under implication
  19. Altmetric Badge
    Chapter 62 Efficient proof encoding
  20. Altmetric Badge
    Chapter 63 Learning Logic programs with random classification noise
  21. Altmetric Badge
    Chapter 64 Handling Quantifiers in ILP
  22. Altmetric Badge
    Chapter 65 Learning from positive data
  23. Altmetric Badge
    Chapter 66 λ-Subsumption and its application to learning from positive-only examples
Attention for Chapter 65: Learning from positive data
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

twitter
1 X user
wikipedia
2 Wikipedia pages

Citations

dimensions_citation
16 Dimensions

Readers on

mendeley
62 Mendeley
citeulike
4 CiteULike
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Chapter title
Learning from positive data
Chapter number 65
Book title
Inductive Logic Programming
Published in
Lecture notes in computer science, August 1996
DOI 10.1007/3-540-63494-0_65
Book ISBNs
978-3-54-063494-2, 978-3-54-069583-7
Authors

Stephen Muggleton

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Belgium 2 3%
United Kingdom 2 3%
Germany 1 2%
Netherlands 1 2%
Portugal 1 2%
Brazil 1 2%
Canada 1 2%
Italy 1 2%
Denmark 1 2%
Other 3 5%
Unknown 48 77%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 24%
Student > Ph. D. Student 14 23%
Professor > Associate Professor 9 15%
Student > Master 6 10%
Professor 4 6%
Other 5 8%
Unknown 9 15%
Readers by discipline Count As %
Computer Science 42 68%
Psychology 3 5%
Engineering 3 5%
Biochemistry, Genetics and Molecular Biology 1 2%
Business, Management and Accounting 1 2%
Other 3 5%
Unknown 9 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 April 2020.
All research outputs
#6,413,521
of 22,787,797 outputs
Outputs from Lecture notes in computer science
#2,106
of 8,126 outputs
Outputs of similar age
#7,947
of 29,850 outputs
Outputs of similar age from Lecture notes in computer science
#2
of 7 outputs
Altmetric has tracked 22,787,797 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 8,126 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 72% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 29,850 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.