Minimum mean square estimation and neural networks

@article{Manry1996MinimumMS,
  title={Minimum mean square estimation and neural networks},
  author={Michael T. Manry and Steven J. Apollo and Qiang Yu},
  journal={Neurocomputing},
  year={1996},
  volume={13},
  pages={59-74}
}
Neural networks for estimation, such as the multilayer perceptron (MLP) and functional link net (FLN), are shown to approximate the minimum mean square estimator rather than the maximum likelihood estimator or others. Cramer-Rao maximum a posteriori lower bounds on estimation error can therefore be used to approximately bound network training error, when a statistical signal model is available for its inputs and the desired outputs are Gaussian. The bounds help the user to determine when to… CONTINUE READING
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