ML Anthology

Learning Logical Definitions from Relations

J. Ross Quinlan
MLJ 1990 pp. 239-266
doi:10.1007/BF00117105 /mlj/1990/quinlan1990mlj-learning/

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.

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Text

Quinlan. "Learning Logical Definitions from Relations." Machine Learning, 1990. doi:10.1007/BF00117105

Markdown

[Quinlan. "Learning Logical Definitions from Relations." Machine Learning, 1990.](https://mlanthology.org/mlj/1990/quinlan1990mlj-learning/) doi:10.1007/BF00117105

BibTeX

@article{quinlan1990mlj-learning,
  title     = {{Learning Logical Definitions from Relations}},
  author    = {Quinlan, J. Ross},
  journal   = {Machine Learning},
  year      = {1990},
  pages     = {239-266},
  doi       = {10.1007/BF00117105},
  volume    = {5},
  url       = {https://mlanthology.org/mlj/1990/quinlan1990mlj-learning/}
}