In this paper we present the results of a set of experiments we conducted in order to evaluate the viability of the behavioral synthesis, relying on the tools available at the mom...
This paper takes a computational learning theory approach to a problem of linear systems identification. It is assumed that inputs are generated randomly from a known class consist...
This paper introduces a method for regularization of HMM systems that avoids parameter overfitting caused by insufficient training data. Regularization is done by augmenting the E...