Title :
A visual neural classifier
Author :
Ornes, Chester ; Sklansky, Jack
Author_Institution :
Dept. of Electr. & Comput. Eng., California Univ., Irvine, CA, USA
fDate :
8/1/1998 12:00:00 AM
Abstract :
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
Keywords :
data visualisation; image segmentation; learning (artificial intelligence); self-organising feature maps; Kohunen´s self-organizing map; classifier-assisted decisions; decision regions; image segmentation; low error rates; medical diagnosis; synthetic data; training set; visual neural classifier; visualization; Data visualization; Displays; Image segmentation; Lifting equipment; Medical diagnosis; Neck; Neural networks; Neurons; Nonhomogeneous media; Training data;
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
DOI :
10.1109/3477.704302
