A new neural classifier allows visualization of the training set and d
ecision 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 perform
ance to Kohonen's self organizing map, We show in applications to imag
e segmentation and medical diagnosis that visualization enables a desi
gner to refine the classifier to achieve low error rates and enhances
a user's ability to make classifier-assisted decisions.