An Instance-Based Learning Method for Database: An Information Theoretic Approach
Abstract
A new method of instance-based learning for databases is proposed. We improve the current similarity measures in several ways using information theory. Similarity is defined on every possible attribute type in a database, and also the weight of each attribute is calculated automatically by the system. Besides, our nearest neighbor algorithm assigns different weights to the selected instances. Our system is implemented and tested on a typical machine learning database.
Cite
Text
Lee. "An Instance-Based Learning Method for Database: An Information Theoretic Approach." European Conference on Machine Learning, 1994. doi:10.1007/3-540-57868-4_80Markdown
[Lee. "An Instance-Based Learning Method for Database: An Information Theoretic Approach." European Conference on Machine Learning, 1994.](https://mlanthology.org/ecmlpkdd/1994/lee1994ecml-instancebased/) doi:10.1007/3-540-57868-4_80BibTeX
@inproceedings{lee1994ecml-instancebased,
title = {{An Instance-Based Learning Method for Database: An Information Theoretic Approach}},
author = {Lee, Changhwan},
booktitle = {European Conference on Machine Learning},
year = {1994},
pages = {387-390},
doi = {10.1007/3-540-57868-4_80},
url = {https://mlanthology.org/ecmlpkdd/1994/lee1994ecml-instancebased/}
}