A New Cost Function for Binary Classification Problems Based on the Distributions of the Soft Output for Each Class

This paper proposes a new cost function for supervised training of neural networks in binary classification applications. This cost function aims at reducing the probability of classification error by reducing the overlap between distributions of the soft output for each class. The non parametric Parzen window method, with Gaussian kernels, is used to estimate the distributions from the training data set. The cost function has been implemented in a GRBF neural network and has been tested in a motion detection application from low resolution infrared images, showing some advantages with respect to the conventional mean squared error cost function and also with respect to the support vector machine, a reference binary classifier.