Inductive Learning of Characteristic Concept Description from Small Sets of Classified Examples
Abstract
This paper presents a novel idea to the problem of learning concept descriptions from examples. Whereas most existing approaches rely on a large number of classified examples, the approach presented in the paper is aimed at being applicable when only a few examples are classified as positive (and negative) instances of a concept. The approach tries to take advantage of the information which can be induced from descriptions of unclassified objects using a conceptual clustering algorithm. The system Cola is described and results of applying Cola in two real-world domains are presented.
Cite
Text
Emde. "Inductive Learning of Characteristic Concept Description from Small Sets of Classified Examples." European Conference on Machine Learning, 1994. doi:10.1007/3-540-57868-4_53Markdown
[Emde. "Inductive Learning of Characteristic Concept Description from Small Sets of Classified Examples." European Conference on Machine Learning, 1994.](https://mlanthology.org/ecmlpkdd/1994/emde1994ecml-inductive/) doi:10.1007/3-540-57868-4_53BibTeX
@inproceedings{emde1994ecml-inductive,
title = {{Inductive Learning of Characteristic Concept Description from Small Sets of Classified Examples}},
author = {Emde, Werner},
booktitle = {European Conference on Machine Learning},
year = {1994},
pages = {103-121},
doi = {10.1007/3-540-57868-4_53},
url = {https://mlanthology.org/ecmlpkdd/1994/emde1994ecml-inductive/}
}