Regularizing AdaBoost

Rätsch, G; Onoda, T; Müller, K-R

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Regularizing AdaBoost

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https://papers.nips.cc/paper/1615-regularizing-adaboost.pdf
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Rätsch, G., Onoda, T., & Müller, K.-R. (1999). Regularizing AdaBoost. In M. Kearns, S. Solla, & D. Cohn (Eds.), Advances in Neural Information Processing Systems 11 (pp. 564-570). Cambridge, MA, USA: MIT Press.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-E69B-8

Abstract

Boosting methods maximize a hard classification margin and are known as powerful techniques that do not exhibit overfitting for low noise cases. Also for noisy data boosting will try to enforce a hard margin and thereby give too much weight to outliers, which then leads to the dilemma of non-smooth fits and overfitting. Therefore we propose three algorithms to allow for soft margin classification by introducing regularization with slack variables into the boosting concept: (1) AdaBoost reg and regularized versions of (2) linear and (3) quadratic programming AdaBoost. Experiments show the usefulness of the proposed algorithms in comparison to another soft margin classifier: the support vector machine.