Neural Computation, Vol 9, 1345-1355, Copyright © 1997 by The MIT Press
Self-Organizing Map with Dynamical Node Splitting: Application to Handwritten Digit Recognition
Sung-Bae Cho
This article presents a simple yet elegant pattern recognizer based on a
dynamic node-splitting scheme for the self-organizing mapthat can adapt its
structure as well as its weights. The scheme makes use of a structure
adaptation capability to place the nodes of prototype vectors into the
pattern space accurately so as to make the decision boundaries as close to
the class boundaries as possible. In order to show the performance of the
proposed scheme, experiments with the unconstrained handwritten digit
database of Concordia University in Canada were conducted. The proposed
method for an incremental formation of feature maps is 96.05 percent of the
recognition rate. In view of the elegant simplicity of the approach, the
reported performance is remarkable and can stand up to one of the best
results reported in the literature with the same database.
