  In this paper we explore the role of sample mean in building a neural network
for classification. This role is surprisingly extensive and includes: direct
computation of weights without training, performance monitoring for samples
without known classification, and self-training for unlabeled data.
Experimental computation on a CIFAR-10 data set provides promising empirical
evidence on the efficacy of a simple and widely applicable approach to some
difficult problems.
