Toward an Ecplanatory Similarity Measure for Nearest-Neighbor Classification
Abstract
In this paper, a new similarity measure for nearest-neighbor classification is introduced. This measure is an approximation of a theoretical similarity that has some interesting properties. In particular, this latter is a step toward a theory of concepts formation. It renders identical some examples that have distinct representations. Moreover, these examples share some properties relevant for the concept undertaken. Hence, a rule-based representation of the concept can be inferred from the theoretical similarity. Moreover, in this paper, the approximation is validated by some preliminary experiments on non-noisy datasets.
Cite
Text
Latourrette. "Toward an Ecplanatory Similarity Measure for Nearest-Neighbor Classification." European Conference on Machine Learning, 2000. doi:10.1007/3-540-45164-1_25Markdown
[Latourrette. "Toward an Ecplanatory Similarity Measure for Nearest-Neighbor Classification." European Conference on Machine Learning, 2000.](https://mlanthology.org/ecmlpkdd/2000/latourrette2000ecml-ecplanatory/) doi:10.1007/3-540-45164-1_25BibTeX
@inproceedings{latourrette2000ecml-ecplanatory,
title = {{Toward an Ecplanatory Similarity Measure for Nearest-Neighbor Classification}},
author = {Latourrette, Mathieu},
booktitle = {European Conference on Machine Learning},
year = {2000},
pages = {238-245},
doi = {10.1007/3-540-45164-1_25},
url = {https://mlanthology.org/ecmlpkdd/2000/latourrette2000ecml-ecplanatory/}
}