How to combine several weak classifier into one strong classifier with AdaBoost algorithm?

Question

I have question about how AdaBoost combines weak classifiers for each iteration, into a strong classifier. I use C4.5 algorithm as weak classifier algorithm. And for each iteration it produced different decision tree and alpha. How can I combine those models into one strong classifier. In the algorithm has been told that to combine them, adaboost uses formula alpha*hyphotesis. how can I combine them with that formula?

Answer 1

It's easy. alpha can be calculated in different way. Viola in his paper said:

alpha= log(1/beta).
beta= wr/(1-wr).
wr is weighted error.