Boosting is an ensemble-learning method that combines weak prediction rules into a weighted strong predictor. AdaBoost repeatedly increases the weights of misclassified observations and chooses each new rule using the resulting weighted sample. Its final binary classifier is the sign of the weighted sum of the weak classifiers.
Boosting
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References
Freund, Y. and Schapire, R. E. "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting." J. Comput. System Sci. 55, 119-139, 1997. https://doi.org/10.1006/jcss.1997.1504.Cite this as:
Weisstein, Eric W. "Boosting." From MathWorld--A Wolfram Resource. https://mathworld.wolfram.com/Boosting.html