Analog circuit implementation and learning algorithm for nearest neighbor classifiers

Analog electronic circuits are implemented and learning algorithms are presented for Nearest Neighbor (NN) and f-NN. classifiers on the basis of the probabilistic formulation of these classifiers. Electronic networks are compact subthreshold MOS transistor circuits. In the learning algorithm, the place of prototypes and the variance of the probability distribution are optimized by using the steepest descent method for the Kullback-Leibler's information between the network output and the correct membership.