See also a similar question on stats.SE.
The classifiers it uses can be weak (i.e., display a substantial error rate), but as long as their performance is not random (resulting in an error rate of 0.5 for binary classification), they will improve the final model. Even classifiers with an error rate higher than would be expected from a random classifier will be useful, since they will have negative coefficients in the final linear combination of classifiers and hence behave like their inverses.
What are the benefits of using weak as opposed to strong learners? (e.g. why not boost with "strong" learning methods - are we more prone to overfitting?)
Is there some sort of "optimal" strength for the weak learners? And is this related to the number of learners in the ensemble?
Is there any theory to back up the answers to these questions?