  We consider infinitely dimensional classes of functions and instead of the
relative error setting, which was used in previous papers on the integral norm
discretization, we consider the absolute error setting. We demonstrate how
known results from two areas of research -- supervised learning theory and
numerical integration -- can be used in sampling discretization of the square
norm on different function classes. We prove a general result, which shows that
the sequence of entropy numbers of a function class in the uniform norm
dominates, in a certain sense, the sequence of errors of sampling
discretization of the square norm of this class. Then we use this result for
establishing new error bounds for sampling discretization of the square norm on
classes of multivariate functions with mixed smoothness.
