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A new training algorithm for neural networks in binary classification problems is presented. It is based on the minimization of an estimate of the Bayes risk by using Parzen windows applied to the final one-dimensional nonlinear transformation of the samples to estimate the probability of classification error. This leads to a very general approach to error minimization and training, where the risk...
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