Adaboost

Steps

1. Initialize very sample weight to $\frac{1}{N}$
2. Iterate M times. Each time, change the the training data weights according to training error rates $e_m$. The basic rules are: increases the weights for mis-classified data and reduce the weights of correctly classified data.
3. According to the weights of all weak learners $a_m$, combine all $M$ learners together, then finally give a output as $G(X) = \sum_{m=1}^{M}a_mG_m(x)$

Derivation

Please refer to references!

[1] Adaboost - 新的角度理解权值更新策略 in Chinese [2] Adaboost Wikipedia