Abstract :
Viewing pattern recognition as a problem in statistical classification wherein an n-dimensional hypercube is partitioned into category regions with decision boundaries, this paper focuses on a class of nonlinear boundary forms and describes a variable-structure system which is capable of evolving these boundaries adaptively. Most of the present adaptive recognition systems have a priori fixed structure, usually corresponding to a linear decision procedure, and adaptation is performed by parameter optimization. It is apparent that in practice the simple linear system frequently will be an inadequate approximation to the desired boundaries. The central problem in a more general recognition procedure is the selection and analysis of suitable nonlinear relations among measurements. In the system proposed in this paper, the structure adaptation develops as follows: the recognition network initially assumes a linear structure, a set of relations among the input measurements is generated and selected on the basis of a set of design data, and to accomodate these relations, the structure changes to nonlinear. By repetitively generating and selecting additional relations among the measurements, the structure gradually adjusts itself within the class of allowable structures toward an optimal configuration. By means of simulation on a digital computer, the procedure for structure adaptation was successfully applied to a number of practical problems, handwritten numerals, spoken vowels, and electroencephalograms.
