Techniques like Adaboost use a ensemble of weak classifiers to obtain a "better" classifier.
Does(Can) the final classifier have a greater VC-dimension than the weak classifier?
An intuitive explanation would suffice.
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1$\begingroup$ Well, just thinking about the definition of VC-dimension, do you think (say) boosted decision stumps can shatter a larger number of points than a single decision stump? $\endgroup$ – guy Nov 29 '13 at 22:31
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It depends on the ensemble method you use. Usually the VC-dimension increases. But in the case of AdaBoost, you can find the answer here: http://www.cs.princeton.edu/courses/archive/spr08/cos511/scribe_notes/0305.pdf http://cseweb.ucsd.edu/~yfreund/papers/IntroToBoosting.pdf
