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Two Variations on Fisher's Linear Discriminant for Pattern Recognition
February 2002 (vol. 24 no. 2)
pp. 268-273

Abstract—Discriminants are often used in pattern recognition to separate clusters of points in some multidimensional “feature” space. This paper provides two fast and simple techniques for improving on the classification performance provided by Fisher's linear discriminant for two classes. Both of these methods are also extended to nonlinear decision surfaces through the use of Mercer kernels.

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Index Terms:
Linear discriminant, classification.
Citation:
Tristrom Cooke, "Two Variations on Fisher's Linear Discriminant for Pattern Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 2, pp. 268-273, Feb. 2002, doi:10.1109/34.982904
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