Adaptive Systems in Pattern Recognition

The perceptual superiority of the human visual system over automata is outlined comparing the properties of both systems. The most effective property with regard to pattern recognition is the internal adaptability and the ability of abstracting. Both properties are well performed by human beings. A mechanical perceptor for complex pattern recognition must also have these capabilities. The use of adaptation for pattern recognition is discussed. The realization of these properties by machines is difficult, especially the development of an adequate feature generator which performs the internal adaptability and thus solves the problem of identification-criteria invariance of patterns. This is assumed to be the main task in pattern recognition research. External teaching processes may be accomplished by adaptive categorizers. The existing classification methods are outlined and discussed with regard to adaptive systems. Adaptive categorizers of a learning matrix type and a perceptron type are compared as to structure, linear classification performance, and training routine. It is assumed, however, that the somewhat passive external adaptation of categorizers must be supplemented by a more active adaptation by the system itself.