  We obtain error approximation bounds between expected suprema of canonical
processes that are generated by random vectors with independent coordinates and
expected suprema of Gaussian processes. In particular, we obtain a sharper
proximity estimate for Rademacher and Gaussian complexities. Our estimates are
dimension-free, and depend only on the geometric parameters and the numerical
complexity of the underlying index set.
