Abstract
Structurization of the covariance matrices helps to reduce a number of parameters to be estimated. Own assumptions on the structure of the matrix are correct the structurization of the covariance matrix helps to reduce the generalization error in small learning-set cases. Efficacy of the matrix structurization increases if one decorrelates and scales the data, and uses the optimally stopped single layer perception classifier afterwards.
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Deev, A.D. (1974). Discriminant function designed on independent blocks of variables.-Proc. Acad. of Sci. of USSR, Eng. Cybernetics, (USSR J.), 12,. 153–156 (in Russian).
Friedman J.M. (1989). Regularized discriminant analysis. J. American Statistical Association, 84, 165–175.
McLachlan, G.J.(1992). Discriminant Analysis and Statistical Pattern Recognition. Willey.
Mcshalkin, L.D., & Serdobolskij, V.I. (1978). Errors in classifying multivariate observations. Theory of Probabilities and Applications, 23(4), 772–781 (in Russian)
Raudys, S. (1972). On the amount of a priori information in designing the classification algorithm. Proc. Acad. of Sci. of USSR, Eng. Cybernetics, 14, 168–174 (in Russian).
Raudys, S. (1991). Methods for overcoming dimensionality problems in statistical pattern recognition. A review. Zavodskaya Laboratorya (Factory Lab., USSR Journal), Moscow: Nauka, 3, 45 & 49-55 (in Russian).
Raudys, S. (1996). Linear classifiers in perception design, ICPR13, Proc. 13th Int. Conf. on Pattern Recognition (Vienna, Austria, Aug.25–29) Vol. 4, Track D: Parallel and Connectionist Systems, IEEE Computer Society Press, Los Alamitos, 1996, 763-767.
Raudys, S, (1998), Evolution and generalization of a single neurone. Part l. SLP as Seven statistical classifiers. Neural Networks (accepted).
Raudys, S. & Jain, A.K. (1991). Small sample size effects in statistical pattern recognition: Recommendations for practitioners-IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-13, 252–264.
Raudys, S. & Pikelis, V. (1980). Ondimensionality, sample size, classification error and complexity of the classification algorithm in pattern recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-2 (3), 242–252.
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Raudys, Š., Saudargiene, A. (1998). Structures of the covariance matrices in the classifier design. In: Amin, A., Dori, D., Pudil, P., Freeman, H. (eds) Advances in Pattern Recognition. SSPR /SPR 1998. Lecture Notes in Computer Science, vol 1451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033282
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DOI: https://doi.org/10.1007/BFb0033282
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