  Learning behavior of simple perceptrons is analyzed for a teacher-student
scenario in which output labels are provided by a teacher network for a set of
possibly correlated input patterns, and such that teacher and student networks
are of the same type. Our main concern is the effect of statistical
correlations among the input patterns on learning performance. For this
purpose, we extend to the teacher-student scenario a methodology for analyzing
randomly labeled patterns recently developed in {\em J. Phys. A: Math. Theor.}
{\bf 41}, 324013 (2008). This methodology is used for analyzing situations in
which orthogonality of the input patterns is enhanced in order to optimize the
learning performance.
