A Study of How Domain Knowledge Improves Knowledge-Based Learning Systems
Abstract
A knowledge-based learning system is developed to demonstrate that intelligently selecting a subset of examples based on domain knowledge for rule induction can be more productive than using all provided examples. Knowledge-based learning uses an incomplete domain theory and examples to induce knowledge missing in the theory. Knowledge-based learning systems determine where knowledge is missing in a domain theory, select a relevant subset of examples to use in induction, and induce new rules or modify existing rules.
Cite
Text
Whitehall and Lu. "A Study of How Domain Knowledge Improves Knowledge-Based Learning Systems." International Conference on Machine Learning, 1991. doi:10.1016/B978-1-55860-200-7.50114-8Markdown
[Whitehall and Lu. "A Study of How Domain Knowledge Improves Knowledge-Based Learning Systems." International Conference on Machine Learning, 1991.](https://mlanthology.org/icml/1991/whitehall1991icml-study/) doi:10.1016/B978-1-55860-200-7.50114-8BibTeX
@inproceedings{whitehall1991icml-study,
title = {{A Study of How Domain Knowledge Improves Knowledge-Based Learning Systems}},
author = {Whitehall, Bradley L. and Lu, Stephen C. Y.},
booktitle = {International Conference on Machine Learning},
year = {1991},
pages = {559-563},
doi = {10.1016/B978-1-55860-200-7.50114-8},
url = {https://mlanthology.org/icml/1991/whitehall1991icml-study/}
}