Incremental Learning of Rules and Meta-Rules
Abstract
This paper presents a general incremental learning scheme: a single generalization algorithm can both earn a set of rules from a set of examples, and achieve the refinement of a previous set of rules. This approach is based on a redescription operator called reduction: from a set of examples and a set of rules, we derive a new set of examples describing the behavior of the rule set. New rules are extracted from these behavioral examples: those rules can be seen as meta-rules, as they control previous rules in order to improve their predictive accuracy.
Cite
Text
Schoenauer and Sebag. "Incremental Learning of Rules and Meta-Rules." International Conference on Machine Learning, 1990. doi:10.1016/B978-1-55860-141-3.50010-9Markdown
[Schoenauer and Sebag. "Incremental Learning of Rules and Meta-Rules." International Conference on Machine Learning, 1990.](https://mlanthology.org/icml/1990/schoenauer1990icml-incremental/) doi:10.1016/B978-1-55860-141-3.50010-9BibTeX
@inproceedings{schoenauer1990icml-incremental,
title = {{Incremental Learning of Rules and Meta-Rules}},
author = {Schoenauer, Marc and Sebag, Michèle},
booktitle = {International Conference on Machine Learning},
year = {1990},
pages = {49-57},
doi = {10.1016/B978-1-55860-141-3.50010-9},
url = {https://mlanthology.org/icml/1990/schoenauer1990icml-incremental/}
}