  We consider the problem of recovering an unknown vector from noisy data with
the help of projection estimates. The goal is to find a convex combination of
these estimates with the minimal risk. We study an aggregation method based on
the so-called exponential weighting and provide a new upper bound for the mean
square risk of this method.
