Imprecise Concept Learning Within a Growing Language
Abstract
Concepts are initially described in terms of attribute values. Assuming a growing language, concepts already known to the system can be used in describing new concepts. The learning process is based on clustering terms in concept descriptions in order to replace them by shorter higher level terms. Concept descriptions are in a probabilistic DNF form in order to support imprecision. Results of the learning algorithm are optimized concepts descriptions in terms of a growing language, and a concept hierarchy that can be used for further learning and reasoning within the concept knowledge base.
Cite
Text
Ras and Zemankova. "Imprecise Concept Learning Within a Growing Language." International Conference on Machine Learning, 1989. doi:10.1016/B978-1-55860-036-2.50083-7Markdown
[Ras and Zemankova. "Imprecise Concept Learning Within a Growing Language." International Conference on Machine Learning, 1989.](https://mlanthology.org/icml/1989/ras1989icml-imprecise/) doi:10.1016/B978-1-55860-036-2.50083-7BibTeX
@inproceedings{ras1989icml-imprecise,
title = {{Imprecise Concept Learning Within a Growing Language}},
author = {Ras, Zbigniew W. and Zemankova, Maria},
booktitle = {International Conference on Machine Learning},
year = {1989},
pages = {314-319},
doi = {10.1016/B978-1-55860-036-2.50083-7},
url = {https://mlanthology.org/icml/1989/ras1989icml-imprecise/}
}