ML Anthology

Incremental Refinement of Approximate Domain Theories

Ronen Feldman, Alberto M. Segre, Moshe Koppel
ICML 1991 pp. 500-504
doi:10.1016/B978-1-55860-200-7.50102-1 /icml/1991/feldman1991icml-incremental/

Abstract

In this paper we present a framework for performing incremental correction of approximate domain theories. Approximate domain theories are domain theories which are incomplete and/or incorrect. Based on initial information, belief values are assigned to different subsets of each clause in the domain theory. These belief values provide bias towards the correct refinement of the domain theory. We provide an incremental algorithm that refines the domain theory after observing positive and negative exemplars. Our algorithm requires a smaller number of misclassified exemplars than other algorithms presented in the literature.

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Cite

Text

Feldman et al. "Incremental Refinement of Approximate Domain Theories." International Conference on Machine Learning, 1991. doi:10.1016/B978-1-55860-200-7.50102-1

Markdown

[Feldman et al. "Incremental Refinement of Approximate Domain Theories." International Conference on Machine Learning, 1991.](https://mlanthology.org/icml/1991/feldman1991icml-incremental/) doi:10.1016/B978-1-55860-200-7.50102-1

BibTeX

@inproceedings{feldman1991icml-incremental,
  title     = {{Incremental Refinement of Approximate Domain Theories}},
  author    = {Feldman, Ronen and Segre, Alberto M. and Koppel, Moshe},
  booktitle = {International Conference on Machine Learning},
  year      = {1991},
  pages     = {500-504},
  doi       = {10.1016/B978-1-55860-200-7.50102-1},
  url       = {https://mlanthology.org/icml/1991/feldman1991icml-incremental/}
}