Network Game and Boosting
Abstract
We propose an ensemble learning method called Network Boosting which combines weak learners together based on a random graph (network). A theoretic analysis based on the game theory shows that the algorithm can learn the target hypothesis asymptotically. The comparison results using several datasets of the UCI machine learning repository and synthetic data are promising and show that Network Boosting has much resistance to the noisy data than AdaBoost through the cooperation of classifiers in the classifier network.
Cite
Text
Wang and Zhang. "Network Game and Boosting." European Conference on Machine Learning, 2005. doi:10.1007/11564096_44Markdown
[Wang and Zhang. "Network Game and Boosting." European Conference on Machine Learning, 2005.](https://mlanthology.org/ecmlpkdd/2005/wang2005ecml-network/) doi:10.1007/11564096_44BibTeX
@inproceedings{wang2005ecml-network,
title = {{Network Game and Boosting}},
author = {Wang, Shijun and Zhang, Changshui},
booktitle = {European Conference on Machine Learning},
year = {2005},
pages = {461-472},
doi = {10.1007/11564096_44},
url = {https://mlanthology.org/ecmlpkdd/2005/wang2005ecml-network/}
}