In this paper we present a discriminative training procedure for Gaussian mixture densities. Conventional maximum likelihood (ML) training of such mixtures proved to be very efficient for object recognition, even though each class is treated separately in training. Discriminative criteria offer the advantage that they also use out-of-class data, that is they aim at optimizing class separability. We present results on the US Postal Service (USPS) handwritten digits database and compare the discriminative results to those obtained by ML training . We also compare our best results with those reported by other groups, proving them to be state-of-the-art.

Published in 21. DAGM Symposium Mustererkennung, W. Förstner, J. Buhmann, A. Faber, P. Faber (eds.). Bonn, Germany, pp. 205-212, September 1999.

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