Bayesian approach to the pattern recognition problem in nonstationary environment

Abstract

The classical learning problem of the pattern recognition in a finite-dimensional linear space of real-valued features is studied under the conditions of a non-stationary universe. The training criterion of non-stationary pattern recognition is formulated as a generalization of the classical Support Vector Machine. The respective numerical algorithm has the computation complexity proportional to the length of the training time series. © 2011 Springer-Verlag Berlin Heidelberg.