Hoeffding inequality for multiple hypothesis
It's clear for me how inequality works for each hypothesis separately. But I don't understand why we need Hoeffding inequality for multiple hypothesis. If i have training data set of size 'N' then (for fixed tolerance 'e') Hoeffding upper bound is determined for each hypoyhesis. The only thing that remains is to find hypothesis with minimal insample rate. Why do we need to consider all hypothesis simultaneously? What information gives us Hoeffding inequality with factor 'M' in it? I undetstand example with coins but I can not relate it to learning problem.
Sorry for my english and thanks. 
Re: Hoeffding inequality for multiple hypothesis
Thank you, Professor!
I do not quite understand the following: I thought that the goal is to get the upper bound for probability of event . That is, for feasibility of learning the probability of this event should be small. In my opinion two events (mine) and (yours) are different events. Am I right? My last question is as follows. The LHS of Hoeffding inequality for M hypothesis is . It implies that event and event (event if you are right) are equal. Though I understand the meaning of event the meaning of event isn't so clear for me. What it literally means? I think it means . Am I right? 
Re: Hoeffding inequality for multiple hypothesis
Sorry, there was a typo in my previous message. Yes they are different events. But they are very related events.
P[B]=1P[A]>=1M*... Quote:

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