Nonlinear Fisher Discriminant Analysis Using a Minimum Squared Error Cost Function and the Orthogonal Least Squares Algorithm.
Lee, K.L. and Billings, S.
(2000)
Nonlinear Fisher Discriminant Analysis Using a Minimum Squared Error Cost Function and the Orthogonal Least Squares Algorithm.
Research Report.
ACSE Research Report 781
.
Department of Automatic Control and Systems Engineering
Abstract
The nonlinear discriminant function obtained using a minimum squared error cost function can be shown to be directly related to the nonlinear Fisher discriminant. With the squared error cost function, the orthogonal least squares algorithm can be used to find a parsimonious description of the nonlinear discriminant function. Two simple classification techniques will be introduced and tested on a number of real and artificial data sets. The results show that the new classification technique can often perform favourably with other state of the art classification techniques.
Metadata
Item Type:
Monograph
Authors/Creators:
Lee, K.L.
Billings, S.
Copyright, Publisher and Additional Information:
The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instance
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