Automatic design of binary W-operators using artificial feed-forward neural networks based on the weighted mean square error cost function

Abstract

One of the main issues concerning automatic design of W-operators is the one of generalization. Considering the designing of W-operators as a particular case of designing a pattern recognition system, in this work we propose a new approach for the automatic design of binary W-operators. This approach consists on a functional representation of the conditional probabilities for the whole set of patterns viewed by a given window, instead the values of the characteristic function. The estimation of its parameters is achieved by means of a nonlinear regression performed by an artificial feed-forward neural network based on a weighted mean square error cost function. Experimental results show that, for the applications presented in this work, the proposed approach leads to better results than one of the best existing methods of generalization within the family of W-operators, like is the case of pyramidal multiresolution. © 2012 Springer-Verlag.