@inproceedings{f63649aa13fb4aa9a6f4f4af62603e6a,
title = "Boosting and Other Machine Learning Algorithms",
abstract = "In an optical character recognition problem, we compare (as a function of training set size) the performance of three neural network based ensemble methods (two versions of boosting and a committee of neural networks trained independently) to that of a single network. In boosting, the number of patterns actually used for training is a subset of all potential training patterns. Based on either a fixed computational cost or training set size criterion, some version of boosting is best We also compare (for a fixed training set size) boosting to the following algorithms: optimal margin classifiers, tangent distance, local learning, k-nearest neighbor, and a large weight sharing network with the boosting algorithm showing the best performance.",
author = "Harris Drucker and Corinna Cortes and Jackel, {L. D.} and Yann LeCun and Vladimir Vapnik",
note = "Publisher Copyright: {\textcopyright} 1994 Proceedings of the 11th International Conference on Machine Learning, ICML 1994. All rights reserved.; 11th International Conference on Machine Learning, ICML 1994 ; Conference date: 10-07-1994 Through 13-07-1994",
year = "1994",
doi = "10.1016/B978-1-55860-335-6.50015-5",
language = "English (US)",
series = "Proceedings of the 11th International Conference on Machine Learning, ICML 1994",
publisher = "Morgan Kaufmann Publishers, Inc.",
pages = "53--61",
editor = "Cohen, {William W.} and Haym Hirsh",
booktitle = "Proceedings of the 11th International Conference on Machine Learning, ICML 1994",
address = "United States",
}