Description: We have developed a system for learning from relational data (e.g. from graphs). The system is pretty efficient, gives a non traditional type of algorithm ("bottom up") based on the theoretical results, and is a state of the art Inductive Logic Programming (ILP) system. Current work is focused on solving large scale problems involving classification of molecules.
his work is partly supported by NSF grant IIS-0099446.
Authors: M. Arias, R. Khardon and J. Maloberti
Journal of Machine Learning Research
Vol 8, pp549--587
Authors: M. Arias and R. Khardon
In The Proceedings of the International Conference on Inductive Logic Programming
Authors: Khardon, R.
Proceedings of the International Conference on Machine Learning
LogAn-H is a system for learning function free Horn expressions. It is based on provably correct algorithms for learning with queries. More information can be found in:
Theoretical background (correctness and complexity proofs)
Paper describing the system and some experiments
An Example Run
Two variants of the system and algorithms have been implemented (in Prolog and C) as described in the papers.
Data files and code are available (for research purposes) upon request.