An optimal matching-score net for pattern classification
Rui J. P. de Figueiredo · 1990
A novel optimal model is obtained for the matching score subnet of a pattern classifier network by requiring that the model interpolate a set of exemplary input-output pairs while optimizing a min-max criterion in an uncertain set Γ a neural spaceFto which the net is assumed to belong. This criterion is presumed to provide robustness to the optimal model obtained. The optimal model consists of a two-layer feedforward net, the first layer being the same as the matching score layer of the Hamming net, and the second layer being a new layer (absent in the Hamming net) possessing synaptic weights which result from the optimization procedure used. Applications of this net to some typical signal- and image-classification problems are being pursued