  We show that if $F$ is a convex class of functions that is $L$-subgaussian,
the error rate of learning problems generated by independent noise is
equivalent to a fixed point determined by `local' covering estimates of the
class, rather than by the gaussian averages. To that end, we establish new
sharp upper and lower estimates on the error rate for such problems.
