Abstract
A popular procedure for testing a pattern recognition machine is to present the machine with a set of patterns taken from the real world. The proportion of these patterns which are misrecognized or rejected is taken as the estimate of the error probability or rejection probability for the machine. In Part I, this testing procedure is discussed for the cases of unknown and known a priori probabilities of occurrence of the pattern classes. The differences between the tests that should be made in the two cases are noted, and confidence intervals for the test results are indicated. These concepts are applied to various published pattern recognition results by determining the appropriate confidence interval for each result. In Part II, the problem of the optimum partitioning of a sample of fixed size between the design and test phases of a pattern recognition machine is discussed. One important nonparametric result is that the proportion of the total sample used for testing the machine should never be less than that proportion used for designing the machine, and in some cases should be a good deal more.
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