Empirical Support for Winnow and Weighted-Majority Based Algorithms: Results on a Calendar Scheduling Domain
Abstract
In this paper we describe experimental results using Winnow and Weighted-Majority based algorithms (two algorithms highly studied in the theoretical machine learning literature) on a calendar scheduling domain. We show that these algorithms can be quite competitive practically, outperforming the ID3-based approach currently in use by the Calendar Apprentice system in terms of both accuracy and speed, on a large dataset. In addition we show how Winnow can be applied to achieve a good accuracy/coverage tradeoff and we explore issues that arise such as concept drift. We also provide a theoretical analysis of the Winnow variant that we use (which is one especially suited to conditions with string-valued classifications) and an analysis of a policy for discarding predictors in Weighted-Majority that allows it to speed up as it learns.
Cite
Text
Blum. "Empirical Support for Winnow and Weighted-Majority Based Algorithms: Results on a Calendar Scheduling Domain." International Conference on Machine Learning, 1995. doi:10.1016/B978-1-55860-377-6.50017-7Markdown
[Blum. "Empirical Support for Winnow and Weighted-Majority Based Algorithms: Results on a Calendar Scheduling Domain." International Conference on Machine Learning, 1995.](https://mlanthology.org/icml/1995/blum1995icml-empirical/) doi:10.1016/B978-1-55860-377-6.50017-7BibTeX
@inproceedings{blum1995icml-empirical,
title = {{Empirical Support for Winnow and Weighted-Majority Based Algorithms: Results on a Calendar Scheduling Domain}},
author = {Blum, Avrim},
booktitle = {International Conference on Machine Learning},
year = {1995},
pages = {64-72},
doi = {10.1016/B978-1-55860-377-6.50017-7},
url = {https://mlanthology.org/icml/1995/blum1995icml-empirical/}
}