Question

I am trying to understand Adaboost algorithm but i have some troubles. After reading about Adaboost i realized that it is a classification algorithm(somehow like neural network). But i could not know how the weak classifiers are chosen (i think they are haar-like features for face detection) and how finally the H result which is the final strong classifier can be used. I mean if i found the alpha values and compute the H ,how am i going to benefit from it as a value (one or zero) for new images. Please is there an example describes it in a perfect way? i found the plus and minus example that is found in most adaboost tutorials but i did not know how exactly hi is chosen and how to adopt the same concept on face detection. I read many papers and i had many ideas but until now my ideas are not well arranged. Thanks....

Answer 1

Adaboost is aclassification algorithm, it uses weak classifiers (any thing that give more than 50% correct result, better than random). And finally combines them in one strong classifier. The training stages find the alpha variables which computes the H(final result).
H=Sigma(alpha(i)*h(i)) such that h(i) is 1 or zero for two classes problem.
It seems that H is a weighted sum of all the weak features, so when we have a new input(not seen before) we apply the weak classifiers h(i) and multiply them with the correct alphas we get from training stages to get one or zero.
For more clarification see the "The Top Ten Algorithms in Data Mining" book which can be found on gigapeida.com website.

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