Calibrating the performance of neural networks

Abstract

The authors offer a procedure to assess the performance of signal classifiers, including neural networks. An example is described wherein three neural classifiers are tested in their ability to discriminate between a modeled underwater man-made event, real clutter signals, and a modeled quiet ocean background. Empirical studies using neural classifiers on ocean acoustic data are described. Some observations regarding the utility of the outline procedure are offered.<>