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An Adaptive Version of the Boost By Majority Algorithm (2000) [49 citations — 5 self]

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@INPROCEEDINGS{Freund00anadaptive,
    author = {Yoav Freund},
    title = {An Adaptive Version of the Boost By Majority Algorithm},
    booktitle = {In Proceedings of the Twelfth Annual Conference on Computational Learning Theory},
    year = {2000},
    pages = {102--113}
}

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Abstract:

We propose a new boosting algorithm. This boosting algorithm is an adaptive version of the boost by majority algorithm and combines bounded goals of the boost by majority algorithm with the adaptivity of AdaBoost.

Citations

1593	Bagging predictors – Breiman - 1996
1227	A decision-theoretic generalization of on-line learning and an application to boosting – Freund, Schapire - 1997
619	Additive Logistic Regression: a Statistical View of Boosting – Friedman, Hastie, et al. - 2000
506	Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics, 26(5):1651--1686 – Schapire, Freund, et al. - 1998
462	The strength of weak learnability – Schapire - 1990
407	Improved boosting algorithms using confidence-rated predictions – Schapire, Singer - 1999
307	An experimental comparison of three methods for constructing ensembles of decision trees – Dietterich
299	Boosting a weak learning algorithm by majority – Freund - 1995
19	Direct optimization of margins improves generalization in combined classifiers – Mason, Bartlett, et al. - 1998
13	Drifting games – Schapire - 1999
4	classics edition – SIAM - 1992
1	Continuous drifting games – Freund, Opper - 2000
1	Introduction to Numerical Analysis – Stoler, Bulrisch - 1992