Abstract
A new method to maximize the margin of MLP classifier in classification problems is described. This method is based on a new cost function which minimizes the variance of the mean squared error. We show that with this cost function the generalization performance increase. This method is tested and compared with the standard mean square error and is applied to a face detection problem.
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