  Under mild assumptions on the kernel, we obtain the best known error rates in
a regularized learning scenario taking place in the corresponding reproducing
kernel Hilbert space (RKHS). The main novelty in the analysis is a proof that
one can use a regularization term that grows significantly slower than the
standard quadratic growth in the RKHS norm.
