AdaBoost

AdaBoost constructs an additive classifier by repeatedly fitting a base classifier to exponentially reweighted observations. Misclassified observations receive greater weight at the next iteration, and the final score is a nonnegative weighted sum of the base classifiers.

For labels $Y\in \{-1,1\}$ and a real-valued score $f$, the exponential loss is $\varphi (Yf(X))=e^{-Yf(X)}$. Its population and empirical risks are