# Unsupervised learning for nonlinear synthetic discriminant functions

@inproceedings{Fisher1995UnsupervisedLF, title={Unsupervised learning for nonlinear synthetic discriminant functions}, author={John W. Fisher and Jos{\'e} Carlos Pr{\'i}ncipe}, year={1995} }

- Published 1995

It has been shown in previous work 5,12 that the family of filters which includes the minimum average correlation energy (MACE) filter7 can be formulated as a linear associative memory (LAM) 3 preceded by a linear pre-processor which changes depending on the optimization criterion. We have presented a methodology by which the MACE filter and other synthetic discriminant function6 (SDF) filters can be extended to nonlinear processing structures 9 (i.e. nonlinear associative memories) resulting… CONTINUE READING

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