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In some practical classification problems in which the number of instances of a particular class is much lower/higher than the instances of the other classes, one commonly adopted strategy is to train the classifier over a small, balanced portion of the training data set. Although straightforward, this procedure may discard instances that could be important for the better discrimination of the classes,...
A new supervised learning algorithm, SNN/LP, is proposed for Spiking Neural Networks. This novel algorithm uses limited precision for both synaptic weights and synaptic delays; 3 bits in each case. Also a genetic algorithm is used for the supervised training. The results are comparable or better than previously published work. The results are applicable to the realization of large-scale hardware neural...
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