@article {236,
	title = {Bagged Averaging of Regression Models},
	journal = {Artificial Intelligence Applications and Innovations (IFIP International Federation for Information Processing)},
	volume = {204},
	year = {2006},
	month = {2006},
	pages = {53-60},
	publisher = {Springer-Verlag},
	abstract = {Linear regression and regression tree models are among the most known regression models used in the machine learning community and recently many researchers have examined their sufficiency in ensembles. Although many methods of ensemble design have been proposed, there is as yet no obvious picture of which method is best. One notable successful adoption of ensemble learning is the distributed scenario. In this work, we propose an efficient distributed method that uses different subsets of the same training set with the parallel usage of an averaging methodology that combines linear regression and regression tree models. We performed a comparison of the presented ensemble with other ensembles that use either the linear regression or the regression trees as base learner and the performance of the proposed method was better in most cases.},
	doi = {10.1007/0-387-34224-9_7},
	attachments = {http://daisy.cti.gr/files/Bagged Averaging of Regression Models.pdf},
	author = {Kotsiantis, S. and Kanellopoulos, D. and Zaharakis I}
}
