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We present techniques for constructing classifiers that combine statistical information from training data with tangent approximations to known transformations, and we demonstrate the techniques by applying them to a face recognition task. Our approach is to build Bayes classifiers with approximate class-conditional probability densities for measured data. The high dimension of the measurements in modern classification problems such as speech or image recognition makes inferring probabilitydoi:10.1198/1061860032634 fatcat:z62ta5penze4pj77yswzusakfi