Neural Computation, Vol 9, 637-648, Copyright © 1997 by The MIT Press
Supervised Networks Which Self-Organize Class Outputs
Ramesh R. Sarukkai
Supervised, neural network, learning algorithms have proved very successful
at solving a variety of learning problems; however, they suffer from a
common problem of requiring explicit output labels. In this article, it is
shown that pattern classification can be achieved, in a multilayered,
feedforward, neural network, without requiring explicit output labels, by a
process of supervised self-organization. The class projection is achieved
by optimizing appropriate within-class uniformity and between-class
discernibility criteria. The mapping function and the class labels are
developed together iteratively using the derived self-organizing
backpropagation algorithm. The ability of the self-organizing network to
generalize on unseen data is also experimentally evaluated on real data
sets and compares favorably with the traditional labeled super vision with
neural networks. In addition, interesting features emerge out of the
proposed self-organizing supervision, which are absent in conventional
approaches.
