  We address the problem of classification when data are collected from two
samples with measurement errors. This problem turns to be an inverse problem
and requires a specific treatment. In this context, we investigate the minimax
rates of convergence using both a margin assumption, and a smoothness condition
on the boundary of the set associated to the Bayes classifier. We establish
lower and upper bounds (based on a deconvolution classifier) on these rates.
