Given n feature vectors x 1=(x 11,…,x 1p ),..., x n =(x n1,…,x np ) of size p, a vector of class labels y=(y 1,…,y n ), where y i ∈ K = {-1, 1} describes the class to which the feature vector x i belongs, and a weak learner algorithm, the problem is to build an AdaBoost classifier.
Training Stage
The following scheme shows the major steps of the algorithm:
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Initialize weights D 1(i) = 1/n for i = 1,...,n
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For t = 1,...,T:
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Train the weak learner h t (t) ∈ {-1, 1} using weights D t
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Choose a confidence value α t
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Update
where Z t is a normalization factor
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Output the final hypothesis:
Prediction Stage
Given the AdaBoost classifier and r feature vectors x 1,…,x r , the problem is to calculate the final class
