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}
}
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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