A comparative study of some non-parametric spectral classifiers. Applications to problems with high-overlapping training sets
Francisco J. Cortijo, Nicolás Pérez de la Blanca · International Journal of Remote Sensing · 1997
In this paper we show some alternative classifiers to the widely used maximum likelihood (ML) classifier in order to obtain high accuracy classifications. The ML classifier does not provide high accuracy classifications when the training sets are high-overlapping in the representation space due to the shape of the decision boundaries it imposes. In these cases, it is preferred to adopt another classifier that may adjust the decision boundaries in a better fashion. This objective may be achieved with several non-parametric classifiers and by using the regularized discriminant classifier, as shown in this paper.