Journal:Informatica
Volume 4, Issues 3-4 (1993), pp. 360–383
Abstract
An analytical equation for a generalization error of minimum empirical error classifier is derived for a case when true classes are spherically Gaussian. It is compared with the generalization error of a mean squared error classifier – a standard Fisher linear discriminant function. In a case of spherically distributed classes the generalization error depends on a distance between the classes and a number of training samples. It depends on an intrinsic dimensionality of a data only via initialization of a weight vector. If initialization is successful the dimensionality does not effect the generalization error. It is concluded advantageous conditions to use artificial neural nets are to classify patterns in a changing environment, when intrinsic dimensionality of the data is low or when the number of training sample vectors is really large.
Journal:Informatica
Volume 3, Issue 3 (1992), pp. 301–337
Abstract
Small training sample effects common in statistical classification and artificial neural network classifier design are discussed. A review of known small sample results are presented, and peaking phenomena related to the increase in the number of features and the number of neurons is discussed.