The class of weighted voting decision rules are studied. The estimates for
probability of erroneous decision are obtained for a number of cases. The "
accelerated perceptron" algorithm for weights correction is proposed and st
udied in learning and self-learning modes. The computer simulation results
are provided. (C) 1998 The Franklin Institute. Published by Elsevier Scienc
e Ltd.