initialize equal weights for all samples

Repeat t = 1,…,T

- learn $f_{t}(x)$ with data weights $\alpha_{i}$
- compute weighted error
- compute coefficient
- $\hat{w_{t}}$ is higher when weighted_error is larger

- recomputed weights $\alpha_{i}$
- 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

## AdaBoost Summary

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