Quote:
Originally Posted by kokrah
The Hoeffding bound for the model H in chapter one, only requires that
we make the assumption that the input examples are a random sample
from the bin; so we can generalize the sample error.
What role does the distribution on X play? It appears to me that we don't need
it. (at least the way the issue of feasibility is setup in chapter 1)
ie. true mismatch ~ sample mismatch.
Thanks.

We need the
existence of the input probability distribution
so that "a random sample" becomes well defined, but we don't need any particular
to do that since any
will correspond to a legitimate
for the bin.