Training neural network classifiers through Bayes risk minimization applying unidimensional Parzen windows

Highlights：

• The main contribution of this manuscript is a new training algorithm for binary classification using neural networks.

• The training algorithm is based on the minimization of an estimate of the Bayes risk.

• Parzen windows method is used to estimate the conditional distributions necessary to compute the probabilities of error included in the Bayes risk.

• A new set of training algorithms emerge from this Bayes risk minimization formulation using Parzen windows.

• Some interesting relationships with classical training methods are discovered.