↓ Skip to main content

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 1 A Personal View of How Best to Apply ILP
  3. Altmetric Badge
    Chapter 2 Agents that Reason and Learn
  4. Altmetric Badge
    Chapter 3 Mining Model Trees: A Multi-relational Approach
  5. Altmetric Badge
    Chapter 4 Complexity Parameters for First-Order Classes
  6. Altmetric Badge
    Chapter 5 A Multi-relational Decision Tree Learning Algorithm – Implementation and Experiments
  7. Altmetric Badge
    Chapter 6 Applying Theory Revision to the Design of Distributed Databases
  8. Altmetric Badge
    Chapter 7 Disjunctive Learning with a Soft-Clustering Method
  9. Altmetric Badge
    Chapter 8 ILP for Mathematical Discovery
  10. Altmetric Badge
    Chapter 9 An Exhaustive Matching Procedure for the Improvement of Learning Efficiency
  11. Altmetric Badge
    Chapter 10 Efficient Data Structures for Inductive Logic Programming
  12. Altmetric Badge
    Chapter 11 Graph Kernels and Gaussian Processes for Relational Reinforcement Learning
  13. Altmetric Badge
    Chapter 12 On Condensation of a Clause
  14. Altmetric Badge
    Chapter 13 A Comparative Evaluation of Feature Set Evolution Strategies for Multirelational Boosting
  15. Altmetric Badge
    Chapter 14 Comparative Evaluation of Approaches to Propositionalization
  16. Altmetric Badge
    Chapter 15 Ideal Refinement of Descriptions in $\mathcal{AL}$ -Log
  17. Altmetric Badge
    Chapter 16 Which First-Order Logic Clauses Can Be Learned Using Genetic Algorithms?
  18. Altmetric Badge
    Chapter 17 Improved Distances for Structured Data
  19. Altmetric Badge
    Chapter 18 Induction of Enzyme Classes from Biological Databases
  20. Altmetric Badge
    Chapter 19 Estimating Maximum Likelihood Parameters for Stochastic Context-Free Graph Grammars
  21. Altmetric Badge
    Chapter 20 Induction of the Effects of Actions by Monotonic Methods
  22. Altmetric Badge
    Chapter 21 Hybrid Abductive Inductive Learning: A Generalisation of Progol
  23. Altmetric Badge
    Chapter 22 Query Optimization in Inductive Logic Programming by Reordering Literals
  24. Altmetric Badge
    Chapter 23 Efficient Learning of Unlabeled Term Trees with Contractible Variables from Positive Data
  25. Altmetric Badge
    Chapter 24 Relational IBL in Music with a New Structural Similarity Measure
  26. Altmetric Badge
    Chapter 25 An Effective Grammar-Based Compression Algorithm for Tree Structured Data
Overall attention for this book and its chapters
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

Mentioned by

twitter
1 X user
wikipedia
6 Wikipedia pages

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
149 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Inductive Logic Programming
Published by
Lecture notes in computer science, January 2003
DOI 10.1007/b13700
ISBNs
978-3-54-020144-1, 978-3-54-039917-9
Authors

Tamás Horváth, Akihiro Yamamoto

Editors

Horváth, Tamás, Yamamoto, Akihiro

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 149 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 149 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 <1%
Researcher 1 <1%
Unknown 147 99%
Readers by discipline Count As %
Computer Science 1 <1%
Engineering 1 <1%
Unknown 147 99%
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 21 October 2022.
All research outputs
#6,671,604
of 23,570,677 outputs
Outputs from Lecture notes in computer science
#2,121
of 8,140 outputs
Outputs of similar age
#25,526
of 131,233 outputs
Outputs of similar age from Lecture notes in computer science
#36
of 92 outputs
Altmetric has tracked 23,570,677 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 8,140 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. 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 131,233 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 92 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.