Informatica

Analysing massive amounts of data and extracting value from it has become key across different disciplines. As the amounts of data grow rapidly, current approaches for data analysis are no longer efficient. This is particularly true for clustering algorithms where distance calculations between pairs of points dominate overall time: the more data points are in the dataset, the bigger the share of time needed for distance calculations.

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.