Training linear neural network with early stopped learning and ridge estimation

Abstract

This paper addresses the problem of supervised learning in layered neural network with linear units and includes an analysis of the effect of noise on training algorithms. We survey most of the known results on linear networks. The connections to classical statistical ideas such as ordinary least squares are emphasized.