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Old 01-04-2018, 02:20 PM
hhprogram hhprogram is offline
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Join Date: Oct 2017
Posts: 7
Default Re: General question on VC bounds in Q9-10

follow up on this question. So, if the final ensemble learned hypothesis set has weights on all the original individual hypothesis sets - does that mean the VC dimension is the union of all the individual hypothesis sets?

It seems in general that ensemble learning might run into the VC dimension / generalization problem (ie similar to 'snooping' when you try a model and then see it doesn't perform well, and then try another model etc..) but since it is used a lot in practice - I'm curious to learn why it doesn't suffer from generalization problems. After doing a little research - is it because generally when using the ensemble learning the individual hypothesis are relatively simple and thus have a low VC dimension (and also perform ok but not great by themselves) therefore, when combining simple models together the VC dimension doesn't get too ridiculous? Thanks
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