@article{Quinlan90Learning,
	title = {Learning Logical Definitions from Relations},
	author = {J. Ross Quinlan},
	journal = {Machine Learning},
	month = {August},
	number = {3},
	pages = {239--266},
	url = {http://dx.doi.org/10.1023/A:1022699322624},
	volume = {5},
	year = {1990},
	biburl = {http://www.bibsonomy.org/bibtex/29e05324f888fabc931403b1211cbcc57/mh},
	description = {SpringerLink - Journal Article},
	abstract = {This paper describes FOIL, a system that learns Horn clauses from
	data expressed as relations. FOIL is based on ideas that have proved
	effective in attribute-value learning systems, but extends them to
	a first-order formalism. This new system has been applied successfully
	to several tasks taken from the machine learning literature. ER -},
	timestamp = {2007.12.19}, owner = {martin},
	keywords = {FOIL ILP Induction empirical_learning first_order_rules inductive_logic_programming inductive_programming relational_data }
}