  The method to derive uniform bounds with Gaussian and Rademacher complexities
is extended to the case where the sample average is replaced by a nonlinear
statistic. Tight bounds are obtained for U-statistics, smoothened L-statistics
and error functionals of l2-regularized algorithms.
