7
$\begingroup$

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.

$\endgroup$
  • 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
2
$\begingroup$

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

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.