Evaluation of the required sample size in some supervised pattern recognition techniques

Abstract

One of the aspects of supervised pattern recognition applications which is often ignored concerns the minimum number of samples necessary to define sufficiently reliable classification rules. For discriminating techniques, criteria are available that can be used to evaluate this minimum sample size. For class-modelling techniques, however, no attention has previously been paid to this aspect. Here, the guidelines that can be applied in the use of a discriminating supervised technique are discussed, and criteria are proposed that can be applied when class-modelling supervised techniques, particularly UNEQ, are applied.