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).