Statistical recognition of multivariate patterns is investigated for the case in which the attribute vectors of patterns have non-Gaussian distributions and only the moments of these distributions are known. A recognition approach based on the approximation of pattern distributions by Gram-Charlier series and use of the Bayes decision rule is developed. New decision rules are designed. A computer-aided modeling experiment is described.
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