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Inductive Logic Programming

24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers

  • Conference proceedings
  • © 2015

Overview

Editors:
  1. Jesse Davis

    1. Department of Computer Science, KU Leuven, Leuven, Belgium

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  2. Jan Ramon

    1. Department of Computer Science, KU Leuven, Leuven, Belgium

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 9046)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

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About this book

This book constitutes the thoroughly refereed post-conference proceedings of the 24th International Conference on Inductive Logic Programming, ILP 2014, held in Nancy, France, in September 2014.

The 14 revised papers presented were carefully reviewed and selected from 41 submissions. The papers focus on topics such as the inducing of logic programs, learning from data represented with logic, multi-relational machine learning, learning from graphs, and applications of these techniques to important problems in fields like bioinformatics, medicine, and text mining.

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Keywords

Table of contents (14 papers)

  1. Front Matter

    Pages I-X
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  2. Reframing on Relational Data

    • Chowdhury Farhan Ahmed, Clément Charnay, Nicolas Lachiche, Agnès Braud
    Pages 1-15

  3. Inductive Learning Using Constraint-Driven Bias

    • Duangtida Athakravi, Dalal Alrajeh, Krysia Broda, Alessandra Russo, Ken Satoh
    Pages 16-32

  4. Nonmonotonic Learning in Large Biological Networks

    • Stefano Bragaglia, Oliver Ray
    Pages 33-48

  5. Construction of Complex Aggregates with Random Restart Hill-Climbing

    • Clément Charnay, Nicolas Lachiche, Agnès Braud
    Pages 49-61

  6. Logical Minimisation of Meta-Rules Within Meta-Interpretive Learning

    • Andrew Cropper, Stephen H. Muggleton
    Pages 62-75

  7. Goal and Plan Recognition via Parse Trees Using Prefix and Infix Probability Computation

    • Ryosuke Kojima, Taisuke Sato
    Pages 76-91

  8. Effectively Creating Weakly Labeled Training Examples via Approximate Domain Knowledge

    • Sriraam Natarajan, Jose Picado, Tushar Khot, Kristian Kersting, Christopher Re, Jude Shavlik
    Pages 92-107

  9. Learning Prime Implicant Conditions from Interpretation Transition

    • Tony Ribeiro, Katsumi Inoue
    Pages 108-125

  10. Statistical Relational Learning for Handwriting Recognition

    • Arti Shivram, Tushar Khot, Sriraam Natarajan, Venu Govindaraju
    Pages 126-138

  11. The Most Probable Explanation for Probabilistic Logic Programs with Annotated Disjunctions

    • Dimitar Shterionov, Joris Renkens, Jonas Vlasselaer, Angelika Kimmig, Wannes Meert, Gerda Janssens
    Pages 139-153

  12. Towards Machine Learning of Predictive Models from Ecological Data

    • Alireza Tamaddoni-Nezhad, David Bohan, Alan Raybould, Stephen Muggleton
    Pages 154-167

  13. PageRank, ProPPR, and Stochastic Logic Programs

    • Dries Van Daele, Angelika Kimmig, Luc De Raedt
    Pages 168-180

  14. Complex Aggregates over Clusters of Elements

    • Celine Vens, Sofie Van Gassen, Tom Dhaene, Yvan Saeys
    Pages 181-193

  15. On the Complexity of Frequent Subtree Mining in Very Simple Structures

    • Pascal Welke, Tamás Horváth, Stefan Wrobel
    Pages 194-209

  16. Back Matter

    Pages 211-211
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Editors and Affiliations

  • Department of Computer Science, KU Leuven, Leuven, Belgium

    Jesse Davis, Jan Ramon

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