Normal Discrimination with Unclassified Observations

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Abstract
Fisher's linear discriminant rule may be estimated by maximum likelihood estimation using unclassified observations. It is shown that the ratio of the relevant information contained in unclassified observations to that in classified observations varies from approximately one-fifth to two-thirds for the statistically interesting range of separation of the populations. Thus, more information may be obtained from large numbers of inexpensive unclassified observations than from a small classified sample. Also, all available unclassified and classified data should be used for estimating Fisher's linear discriminant rule.

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