Quote:
Originally Posted by dsvav
I am confused here , I don't understand what is final hypothesis here.
There are 1000 target function and corresponding 1000 weight vectors/hypothesis in problem 5 .
So for problem 6 , 1000 times I generate 1000 outofsample data and then for each weight vector and target function(from problem 5) I evaluate E_out for that outofsample data and finally average them. This is how I have done.
I don't see final hypothesis here , what I am missing , any hint
Could it be that in problem 5 there is supposed to be only one target function and many insample data ? If so then the final hypothesis/weights could be that produces minimum insample error E_in .
Please clarify.
Thanks a lot.

There is a final hypothesis for each of the 1000 runs. The only reason we are repeating the runs is to average out statistical fluctuations, but all the notions of the learning problem, including the final hypothesis, pertain to a single run.