The last years have witnessed an increasing attention to entropy-based criteria in adaptive systems. Several principles were proposed based on the maximization or minimization of entropic cost functions. We propose a new type of neural network classiers with multilayer perceptron (MLP) architecture, but where the usual mean square error minimization principle is substituted by the minimiza- tion of Shannon's entropy of the dierences between the MLP's out- put and the desired target. The backpropagation algorithm is op- timized with a variable learning rate and tested in v e well known datasets. The results show a very good performance of MLPs trained with Shannon's entropy when compared with the mean square error and cros-entropy criteria.
['Luís M. Silva', 'Joaquim Marques de Sá', 'Luís A. Alexandre']
Neural Network Classification using Shannon's Entropy