A Hybrid Approach of Boosting Against Noisy Data

DOI10.1007/978-3-540-88067-7_3zbMath1171.68447OpenAlexW1530513786MaRDI QIDQ3627899

Nicolas Nicoloyannis, Stéphane Lallich, Maddouri Mondher, Emna Bahri

Publication date: 13 May 2009

Published in: Studies in Computational Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-540-88067-7_3

zbMATH Keywords

Boostingclassifiers aggregation methods

Mathematics Subject Classification ID

Database theory (68P15)

Cites Work

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• Bagging predictors
• The weighted majority algorithm
• Reduction techniques for instance-based learning algorithms
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Improved boosting algorithms using confidence-rated predictions
• Soft margins for AdaBoost

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