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i read about it but i didn't get the idea, and actually i didn't find many pages that talk about uniform noise with boosting, is it rare to happen or what? another question:
i read in some pages that boosting overfit with large number of weak learners because it will get the train error to 0 because it trusted the data too much and be confidence and as a consequence it captured the noise
and in other pages they say that in ONLY boosting as you increase the number of learners and be more confident you will increase the margin due to margin theory. which is correct?

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  • $\begingroup$ Please add links and references to your "some pages" and "other pages" so other know precisely what you are refering to. $\endgroup$ – Winks Jun 14 '16 at 19:24

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