kFOIL: Learning Simple Relational Kernels
Abstract
A novel and simple combination of inductive logic programming with kernel methods is presented. The kFOIL algorithm integrates the well-known inductive logic programming system FOIL with kernel methods. The feature space is constructed by leveraging FOIL search for a set of relevant clauses. The search is driven by the performance obtained by a support vector machine based on the resulting kernel. In this way, kFOIL implements a dynamic propositionalization approach. Both classification and regression tasks can be naturally handled. Experiments in applying kFOIL to well-known benchmarks in chemoinformatics show the promise of the approach. Copyright © 2006, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.
Cite
Text
Landwehr et al. "kFOIL: Learning Simple Relational Kernels." AAAI Conference on Artificial Intelligence, 2006.Markdown
[Landwehr et al. "kFOIL: Learning Simple Relational Kernels." AAAI Conference on Artificial Intelligence, 2006.](https://mlanthology.org/aaai/2006/landwehr2006aaai-kfoil/)BibTeX
@inproceedings{landwehr2006aaai-kfoil,
title = {{kFOIL: Learning Simple Relational Kernels}},
author = {Landwehr, Niels and Passerini, Andrea and De Raedt, Luc and Frasconi, Paolo},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2006},
pages = {389-394},
url = {https://mlanthology.org/aaai/2006/landwehr2006aaai-kfoil/}
}