Performance Analysis of a Probabilistic Inductive Learning System
Abstract
This paper presents a new method for acquiring classificatory knowledge by induction. Based on a probabilistic inference technique, this method allows inherent patterns in noisy training instances to be easily detected. A set of classification rules can then be constructed based on these detected patterns. The proposed method has been evaluated by testing it with some real-world data sets and the results show that it ont-performs some decision-tree based algorithms both in terms of computational efficiency and classification accuracy.
Cite
Text
Chan and Wong. "Performance Analysis of a Probabilistic Inductive Learning System." International Conference on Machine Learning, 1990. doi:10.1016/B978-1-55860-141-3.50006-7Markdown
[Chan and Wong. "Performance Analysis of a Probabilistic Inductive Learning System." International Conference on Machine Learning, 1990.](https://mlanthology.org/icml/1990/chan1990icml-performance/) doi:10.1016/B978-1-55860-141-3.50006-7BibTeX
@inproceedings{chan1990icml-performance,
title = {{Performance Analysis of a Probabilistic Inductive Learning System}},
author = {Chan, Keith C. C. and Wong, Andrew K. C.},
booktitle = {International Conference on Machine Learning},
year = {1990},
pages = {16-23},
doi = {10.1016/B978-1-55860-141-3.50006-7},
url = {https://mlanthology.org/icml/1990/chan1990icml-performance/}
}