#1




Hoeffding inequality and noisy targets
I found the jump from learning a deterministic target function to learning a probability distribution a big jump. The treatment of this concept in the book was a bit too fast for me and not detailed. Also the "intuitive" justification of hoeffding in this case also was not clear to me at all  Hoeffding seems to be a tricky concept in the sense that it's application is prone to error if one is not careful. Is there a more stepbystep explanation of this section somewhere?
One starter question in this regard is that in the basic hoeffding derivation, we have used a binary classifier i.e. the target function returns +/1 (or possibly a multiclass classifier). In the noisy target case should the understanding be that it returns a number 'p' signifying the probability of +1 at x? 
#2




Re: Hoeffding inequality and noisy targets
This lecture
https://www.csie.ntu.edu.tw/~htlin/m...08_handout.pdf contains some materials related to noisy targets, though not a lot. The assumption here is that we still get a deterministic classifier (to predict a noisy target). There are other settings that allow us to get a probabilistic classifier, but those are more complicated to analyze and not discussed in detail here. Hope this helps.
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Tags 
hoeffding's inequality, noisy target 
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