  In the setting of supervised learning using reproducing kernel methods, we
propose a data-dependent regularization parameter selection rule that is
adaptive to the unknown regularity of the target function and is optimal both
for the least-square (prediction) error and for the reproducing kernel Hilbert
space (reconstruction) norm error. It is based on a modified Lepskii balancing
principle using a varying family of norms.
