ML Anthology

Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL

Saso Dzeroski, Nada Lavrac
ICML 1991 pp. 399-402
doi:10.1016/B978-1-55860-200-7.50082-9 /icml/1991/dzeroski1991icml-learning/

Abstract

Algorithms for learning relations have only recently addressed the problem of learning from noisy data. LINUS and FOIL are two such systems, which are based on approaches from attribute-value learning algorithms. The paper presents an empirical comparison of their performance on the problem of learning illegal chess endgame positions from noisy examples.

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Cite

Text

Dzeroski and Lavrac. "Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL." International Conference on Machine Learning, 1991. doi:10.1016/B978-1-55860-200-7.50082-9

Markdown

[Dzeroski and Lavrac. "Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL." International Conference on Machine Learning, 1991.](https://mlanthology.org/icml/1991/dzeroski1991icml-learning/) doi:10.1016/B978-1-55860-200-7.50082-9

BibTeX

@inproceedings{dzeroski1991icml-learning,
  title     = {{Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL}},
  author    = {Dzeroski, Saso and Lavrac, Nada},
  booktitle = {International Conference on Machine Learning},
  year      = {1991},
  pages     = {399-402},
  doi       = {10.1016/B978-1-55860-200-7.50082-9},
  url       = {https://mlanthology.org/icml/1991/dzeroski1991icml-learning/}
}