Normal procedures used for designing a Bayes classifier to classify wheat as the major crop of interest require not only training samples of wheat but also those of nonwheat. Therefore, ground truth must be available for the class of interest plus all confusion classes. The single-class Bayes classifier classifies data into the class of interest or the class "other" but requires training samples only from the class of interest. This paper will present a procedure for Bayes estimation on the µi, Σi, qi (i.e., mean vector, covariance matrix, and a priori probability) of the single-class classifier using labeled samples from the class of interest and unlabeled samples drawn from p(x). The procedure used to derive µi, Σi, and qi is to minimize mL', which is the mean square error of the Bayes decision function of the single-class classifier.
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