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Abstract: Introduction Half&half bagging is a method for producing combinations of classifiers having low generalization error. The basic idea is straightforward and intuitive--suppose k classifiers have been constructed to date. Each classifier was constructed using some weighted subset of the original training set. To construct the next training set, randomly select an example e. Run e down that subgroup of the k classifiers that did not use e in their training sets. Total the unweighted votes of this ... (Update)
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BibTeX entry: (Update)
@misc{ breiman-halfhalf,
author = "Leo Breiman",
title = "Halfhalf Bagging And Hard Boundary Points",
url = "citeseer.ist.psu.edu/breiman98halfhalf.html" }
Citations (may not include all citations):155 An Empirical Comparison of Voting Classification Algorithms:.. - Bauer, Kohavi - 1998
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