Possibilities of Zero-Error Recognition by Dissimilarity Representations

Feature based approaches to pattern recognition suffer from the fact that feature representations of different classes of objects may overlap. This is the consequence of reducing the description of an object to a feature vector. As a result an error free recognition system is even asymptotically (for infinite training sizes) impossible. In this paper it is argued that this limitation does not hold for dissimilarity based representations. Suggestions are made how this may be exploited in practice.

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