Inductive Logic Programming

1. Front Matter

   Pages I-X

   PDF

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

    PDF

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.

• Algorithms
• Artificial intelligence
• Data mining
• Dynamical systems
• Machine learning
• Answer set programming
• Attractors
• Boolean networks
• Clustering
• Constraints
• Dataset shift
• Meta-level constraints
• Probabilistic reasoning
• Program transformation
• Relational learning
• Robotics
• Statistical relational learning
• Stochastic optimization
• Structured data
• Supervised learning

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

  Jesse Davis, Jan Ramon

Policies and ethics