Neural Computation, Vol 10, 2175-2200, Copyright © 1998 by The MIT Press
The Diabolo Classifier
Holger Schwenk
We present a new classification architecture based on autoassociative
neural networks that are used to learn discriminant models of each class.
The proposed architecture has several interesting properties with respect
to other model-based classifiers like nearest-neighbors or radial basis
functions: it has a low computational complexity and uses a compact
distributed representation of the models. The classifier is also well
suited for the incorporation of a priori knowledge by means of a
problem-specific distance measure. In particular, we will show that tangent
distance (Simard, Le Cun, & Denker, 1993) can be used to achieve
transformation invariance during learning and recognition. We demonstrate
the application of this classifier to optical character recognition, where
it has achieved state-of-the-art results on several reference databases.
Relations to other models, in particular those based on principal component
analysis, are also discussed.
