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Pattern Recognition
Volume 27, Issue 4, April 1994, Pages 577-585
 
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doi:10.1016/0031-3203(94)90038-8    How to Cite or Link Using DOI (Opens New Window)
Copyright © 1994 Published by Elsevier Science B.V.

Fractional-power synthetic discriminant functions

J. D. Brasher and J. M. Kinser

Teledyne Brown Engineering, Mail Stop 60, Cummings Research Park, 300 Sparkman Dr. NW, Huntsville, AL 35807-7007, U.S.A.

Received 2 November 1993. 
Available online 19 May 2003.

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Abstract

The standard synthetic discriminant function (SDF) generalizes well. That is, it recognizes objects represented by, but not included in, the training set from which it is synthesized. However, it also correlates with objects not represented by the training set. That is, it does not discriminate well. Conversely, the minimum average correlation energy (MACE) SDF discriminates, but does not generalize, well. By using a power spectrum normalization procedure, a parametric SDF which generalizes better than the MACE SDF and discriminates better than the standard SDF is obtained.

Author Keywords: Synthetic discriminant function; Fractional power; Coefficient rooting; Power spectral density; Generalization; Discrimination

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Pattern Recognition
Volume 27, Issue 4, April 1994, Pages 577-585
 
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