Abstract
Considerable effort in the past few years has been spent in the theoretical development of statistical decision theory.1 In applying this theory to pattern recognition two major problems become immediately apparent. The first problem is to decide which statistical properties are significant (and reasonably independent) for the patterns involved. The second is to choose the means of instrumentation in using these statistics for decision making.

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