Generalization Error of Randomized Linear Zero Empirical Error Classifier: Non-Centered Data Case
V. Lecce Di · 2001
One of the main problems in pattern classification and neural network training theory is the generalization performance of learning. This paper extends the results on randomized lin- ear zero empirical error (RLZEE) classifier obtained by Raudys, Di y ciand Basalykas for the case of centered multivariate spherical normal classes. We derive an exact formula for an expected probability of misclassification (PMC) of RLZEE classifier in a case of arbitrary (centered or non- centered) spherical normal classes. This formula depends on two parameters characterizing the degree of non-centering of data. We discuss theoretically and illustrate graphically and numeri- cally the influence of these parameters on the PMC of RLZEE classifier. In particular, we show that in some cases non-centered data has smaller expected PMC than centered data.