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Old 09-04-2018, 10:44 PM
v_venky v_venky is offline
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Join Date: Sep 2018
Posts: 2
Default 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 step-by-step 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 multi-class classifier). In the noisy target case should the understanding be that it returns a number 'p' signifying the probability of +1 at x?
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