Sequential recognition using a nonparametric ranking procedure

Abstract
In the problems of statistical pattern recognition, it has become increasingly known that there are many applications in which parametric assumptions regarding the pattern statistics are not justified. This paper treats a nonparametric design of a sequential recognition machine which uses the optimum property of Wald's sequential probability ratio test (SPRT). For the case of binary classification, a sequential ranking procedure is found useful in a two-sample problem in which we wish to test the hypothesis against the Lehmann alternatives, based on the successively ranked observations. The test procedure is then analyzed in terms of the performance criterion which in this case is the expected number of observations, with specified error probabilities. A generalization procedure of multiple classification is also given as a direct extension. Computer-simulated experiments have illustrated the effectiveness of this test procedure in the recognition of handwritten English characters, where the nonparametric method seems to be necessary and appropriate.

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