  We present a universally consistent learning rule whose expected error is
monotone non-increasing with the sample size under every data distribution. The
question of existence of such rules was brought up in 1996 by Devroye, Gy\"orfi
and Lugosi (who called them "smart"). Our rule is fully deterministic, a
data-dependent partitioning rule constructed in an arbitrary domain (a standard
Borel space) using a cyclic order. The central idea is to only partition at
each step those cyclic intervals that exhibit a sufficient empirical diversity
of labels, thus avoiding a region where the error function is convex.
