  We show that the lower bound to the critical fraction of data needed to infer
(learn) the orientation of the anisotropy axis of a probability distribution,
determined by Herschkowitz and Opper [Phys.Rev.Lett. 86, 2174 (2001)], is not
always valid. If there is some structure in the data along the anisotropy axis,
their analysis is incorrect, and learning is possible with much less data
points.
