AdaBoost creates a large amount of weak learners, and gives each one prediction score/weight that will be used to combine the learners during evaluation/generalization.
But isn't the point to create different learners that are stronger at different examples? If so, weighting them basd on their overall error is counterproductive. There needs to be a dynamic, state-space framework to ensembling. Is this correct?
I watched the suggested video, but I still do not feel like my question has been answered. If you combine a bunch of weak learners using unchanging coefficients, the error of each individually should be pretty high because they each only target specific aspects of the example distribution.