AdaBoost Summary

1. initialize equal weights for all samples

   $$\alpha_{i} = \frac{1}{N}$$

2. Repeat t = 1,…,T

   • learn $f_{t}(x)$ with data weights $\alpha_{i}$
   • compute weighted error $$weighted_{error_{t}} = \sum_{i=1}^{m}\alpha_{i}I(y_{i} \neq f_{t}(x_{i}))$$
   • compute coefficient $$\hat{w_{t}} = \frac{1}{2}\ln(\frac{1 - weighted_{error_{t}} }{weighted_{error_{t}}})$$
     • $\hat{w_{t}}$ is higher when weighted_error is larger
   • recomputed weights $\alpha_{i}$ $$\alpha_{i} = \begin{equation} \left\{ \begin{array}{lr} \alpha_{i}e^{-\hat{w_{t}}} & \\ \alpha_{i}e^{\hat{w{t}}} & \end{array} \right. \end{equation}$$
   • Normalize weights $\alpha_{i}$
     • if $x_{i}$ often mistake, weight $\alpha_{i}$ gets very large
     • if $x_{i}$ often correct, weight $\alpha_{i}$ gets very small $$\alpha_{i} = \frac{\alpha_{i}}{\sum_{i}^{m}\alpha_{i}}$$

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