First-Order Inductive Learner (FOIL) Algorithm
(Redirected from FOIL)
A First-Order Inductive Learner (FOIL) Algorithm is an inductive logic programming algorithm that uses a top-down greedy search based on a SEQUENTIAL-COVERING algorithm (directed by an information gain heuristic).
- Context:
- It can discovers patterns in the data expressed as First-Order Rules.
- Counter-Example(s):
- See: ID3 Algorithm, SEQUENTIAL-COVERING, Top-Down Learning.
References
2017
- (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/First-order_inductive_learner Retrieved:2017-11-12.
- In machine learning, first-order inductive learner (FOIL) is a rule-based learning algorithm.
2004
- (Melli, 2004) ⇒ Gabor Melli. (2004). “Scribe Notes on FOIL and Inverted Deduction.” In: Scribe Notes for the 2004 SFU course on Machine Learning (SFU CMPT-882 2004).
1990
- (Quinlan, 1990) ⇒ J. Ross Quinlan. (1990). “Learning Logical Definitions from Examples.” In: Machine Learning, 5(3). doi:10.1007/BF00117105
- QUOTE: 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.
