Question: 5 points] In our lecture about AdaBoost algorithm, we introduced the definition of weighted error in each round t. Et 2 where D.(i) is the

5 points] In our lecture about AdaBoost algorithm, we introduced the

5 points] In our lecture about AdaBoost algorithm, we introduced the definition of weighted error in each round t. Et 2 where D.(i) is the weight of i-th training example, and h(xi) is the prediction of the weak classifier learned round t. Note that both yi and ht(x) belong to 1,-1. Prove that equivalently, ithe(i)

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