Generalization Error of Randomized Linear Zero Empirical Error Classifier: Simple Asymptotics for Centered Data Case

An estimation of the generalization performance of classifier is one of most important problems in pattern clasification and neural network training theory. In this paper we estimate the generalization error (mean expected probability of classification) for randomized linear zero empirical error (RLZEE) classifier which was considered by Raudys, Dičiūnas and Basalykas. Instead of “non-explicit” asymptotics of a generalization error of RLZEE classifier for centered multivariate spherically Gaussian classes proposed by Basalykas et al. (1996) we obtain an “explicit” and more simple asymptotics. We also present the numerical simulations illustrating our theoretical results and comparing them with each other and previously obtained results.