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Abstract: Recent research has considered the relationship between boosting and more standard statistical methods, such as logistic regression, concluding that AdaBoost is similar but somehow still very different from statistical methods in that it minimizes a different loss function. In this paper we derive an equivalence between AdaBoost and the dual of a convex optimization problem. In this setting, it is seen that the only difference between minimizing the exponential loss used by AdaBoost and maximum ... (Update)
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BibTeX entry: (Update)
G. Lebanon and J. Lafferty. Boosting and maximum likelihood for exponential models. In Advances in Neural Information Processing Systems, 15, 2001. http://citeseer.ist.psu.edu/lebanon01boosting.html More
@misc{ lebanon01boosting,
author = "G. Lebanon and J. Lafferty",
title = "Boosting and maximum likelihood for exponential models",
text = "G. Lebanon and J. Lafferty. Boosting and maximum likelihood for exponential
models. In Advances in Neural Information Processing Systems, 15, 2001.",
year = "2001",
url = "citeseer.ist.psu.edu/lebanon01boosting.html" }
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