Abstract : This report compares the capabilities and equipment requirements of two kinds of adaptive classification networks--the 'Learning Matric,' and the Madaline. The Learning Matrix and the Madaline are structurally quite similar, forming arrays of binary output signals from linear combinations of binary or analog input signals. Both systems can learn (adjust their own parameters) from sets of patterns (with corresponding desired outputs) presented to them, and they can thereafter successfully classify additional patterns as presented. The two systems differ primarily in their training methods and in their output logic. Both the Learning Matrix and the Madaline can be realized either by the use of adaptive hardware or by digital-computer simulation. The comparison indicates that in most cases the Learning Matrix will require fewer adaptation cycles than will the Madaline when applied to a given problem; on the other hand, the Madaline will usually require less equipment than the Learning Matrix. However, in making a choice of techniques for a given situation, the overriding consideration will usually be the question of optimality. (Author)

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.

The paper gives a survey of the learning circuits which became known as learning matrices and some of their possible technological applications. The first section describes the principle of learning matrices. So-called conditioned connections between the characteristics of an object and the meaning of an object are formed in the learning phase. During the operation of connecting the characteristics of an object with its meaning (EB operation of the knowing phase) upon presenting the object characteristics, the associated most similar meaning is realized in the form of a signal by maximum likelihood decoding. Conversely, in operation from the meaning of an object to its characteristics (BE operation) the associated object characteristics are obtained as signals by parallel reading upon application of an object meaning. According to the characteristic signals processed (binary or analog signals) discrimination must be made between binary and nonbinary learning matrices. In the case of the binary learning matrix the conditioned connections are a statistical measure for the frequency of the coordination of object characteristics and object meaning, in the case of the nonbinary learning matrix they are a measure for an analog value proportional to a characteristic. Both types of matrices allow for the characteristic sets applied during EB operation to be unsystematically disturbed within limits. Moreover, the nonbinary learning matrix is invariant to systematic deviations between presented and learned characteristic sets (invariance to affine transformation, translation and rotated skewness).