THE DIABOLO CLASSIFIER

We present a new classification architecture based on autoassociative neural networks that are used to learn discriminant models of each cla ss. The proposed architecture has several interesting properties with respect to other model-based classifiers like nearest-neighbors or rad ial basis functions: it has a low computational complexity and uses a compact distributed representation of the models. The classifier is al so 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 achi eve transformation invariance during learning and recognition. We demo nstrate the application of this classifier to optical character recogn ition, where it has achieved state-of-the-art results on several refer ence databases. Relations to other models, in particular those based o n principal component analysis, are also discussed.