CLASSIFICATION BY BALANCED, LINEARLY SEPARABLE REPRESENTATION.
|Authors:|| Y. Baram
Classifiers for binary and for real--valued data, consisting of a single internal layer of spherical threshold cells, are completely defined by two fundamental requirements: linear separability of the internal representations, which defines the cells' activation threshold, and input--space covering, which defines the minimal number of cells required. Class assignments are learnt by applying Rosenblatt's learning rule to the internal representations which are balanced, having equally probable bit values. The separation capacity of the proposed classifiers is equal to the size of the internal layer. Generalization is achieved when the data points are clustered. A parallel planar discretization, which is considerably simpler to implement than the spherical one, yields highly unbalanced representations for high resolution of real--valued data, but leads to effective means for classifying symbolic data. Examples of classifying linearly inseparable data, including an application to optical obstacle detection, are also discussed.
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