Abstract
A simple algorithm was developed for estimating optimal linear and quadratic classifiers (OLC & OQC) for non-normal multivariate predictor variables in two-group discriminant analysis. The algorithm is based on the alternating least squares (ALS) principle. The optimal classifiers compared favorably with the linear and quadratic discriminant function (LDF & QDF) methods in true error rate. Possible generalizations of the optimal classifier approach (ridge regression, robust regression based on the weighted least squares, etc.) were discussed.
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