A visual neural classifier.

A new neural classifier allows visualization of the training set and decision regions, providing benefits for both the designer and the user. We demonstrate the visualization capabilities of this visual neural classifier using synthetic data, and compare the visualization performance to Kohunen's self-organizing map. We show in applications to image segmentation and medical diagnosis that visualization enables a designer to refine the classifier to achieve low error rates and enhances a user's ability to make classifier-assisted decisions.