Empirical Support for Winnow and Weighted-Majority Algorithms: Results on a Calendar Scheduling Domain (1995)
Cached
Download Links
- [www.cs.iastate.edu]
- [www-cgi.cs.cmu.edu]
- DBLP
Other Repositories/Bibliography
| Venue: | Machine Learning |
| Citations: | 114 - 4 self |
BibTeX
@INPROCEEDINGS{Blum95empiricalsupport,
author = {Avrim Blum},
title = {Empirical Support for Winnow and Weighted-Majority Algorithms: Results on a Calendar Scheduling Domain},
booktitle = {Machine Learning},
year = {1995},
pages = {64--72},
publisher = {Morgan Kaufmann}
}
Years of Citing Articles
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
This paper describes experimental results on using Winnow and Weighted-Majority based algorithms on a real-world calendar scheduling domain. These two algorithms have been highly studied in the theoretical machine learning literature. We show here that these algorithms can be quite competitive practically, outperforming the decision-tree approach currently in use in the Calendar Apprentice system in terms of both accuracy and speed. One of the contributions of this paper is a new variant on the Winnow algorithm (used in the experiments) that is especially suited to conditions with stringvalued classifications, and we give a theoretical analysis of its performance. In addition we show how Winnow can be applied to achieve a good accuracy/coverage tradeoff and explore issues that arise such as concept drift. We also provide an analysis of a policy for discarding predictors in Weighted-Majority that allows it to speed up as it learns. Keywords: Winnow, Weighted-Majority, Multiplicative alg...
