Conceptual Clustering of Explanations
Abstract
Inductive and explanation-based learning methods traditionally differ in the extent that they exploit background knowledge. Each is search intensive and sensitive to domain imperfections, albeit in different ways. Hybrid systems that abstract over explanations promise the advantages of each approach, but they require augmentation if their promise is to be fully realized. Conceptual clustering can be profitably applied to the abstraction and organization of explanations so that they may be efficiently and appropriately reused.
Cite
Text
Yoo and Fisher. "Conceptual Clustering of Explanations." International Conference on Machine Learning, 1989. doi:10.1016/B978-1-55860-036-2.50006-0Markdown
[Yoo and Fisher. "Conceptual Clustering of Explanations." International Conference on Machine Learning, 1989.](https://mlanthology.org/icml/1989/yoo1989icml-conceptual/) doi:10.1016/B978-1-55860-036-2.50006-0BibTeX
@inproceedings{yoo1989icml-conceptual,
title = {{Conceptual Clustering of Explanations}},
author = {Yoo, Jungsoon P. and Fisher, Douglas H.},
booktitle = {International Conference on Machine Learning},
year = {1989},
pages = {8-10},
doi = {10.1016/B978-1-55860-036-2.50006-0},
url = {https://mlanthology.org/icml/1989/yoo1989icml-conceptual/}
}