The function to
maximize during each input phase is a modified correlation between candidate
hidden unit activation and network error. The absolute covariance between
hidden unit activation (h) and network error (e) is summed across patterns
(p) and is also summed across output units (o) and then standardized by the
sum of squared error deviations. Terms in angled brackets are means.

Both error
minimization and correlation maximization use the same algorithm, called Quickprop
(Fahlman, 1988).