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# comp.ai.neural-nets FAQ, Part 2 of 7: LearningSection - What is GRNN?

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```
GRNN or "General Regression Neural Network" is Donald Specht's term for
Nadaraya-Watson kernel regression, also reinvented in the NN literature by
Schi\oler and Hartmann. (Kernels are also called "Parzen windows".) You can
think of it as a normalized RBF network in which there is a hidden unit
centered at every training case. These RBF units are called "kernels" and
are usually probability density functions such as the Gaussian. The
hidden-to-output weights are just the target values, so the output is simply
a weighted average of the target values of training cases close to the given
input case. The only weights that need to be learned are the widths of the
RBF units. These widths (often a single width is used) are called "smoothing
parameters" or "bandwidths" and are usually chosen by cross-validation or by
more esoteric methods that are not well-known in the neural net literature;

GRNN is a universal approximator for smooth functions, so it should be able
to solve any smooth function-approximation problem given enough data. The
main drawback of GRNN is that, like kernel methods in general, it suffers
badly from the curse of dimensionality. GRNN cannot ignore irrelevant inputs
without major modifications to the basic algorithm. So GRNN is not likely to
be the top choice if you have more than 5 or 6 nonredundant inputs.

References:

Caudill, M. (1993), "GRNN and Bear It," AI Expert, Vol. 8, No. 5 (May),
28-33.

Haerdle, W. (1990), Applied Nonparametric Regression, Cambridge Univ.
Press.

Masters, T. (1995) Advanced Algorithms for Neural Networks: A C++
Sourcebook, NY: John Wiley and Sons, ISBN 0-471-10588-0

Nadaraya, E.A. (1964) "On estimating regression", Theory Probab. Applic.
10, 186-90.

Schi\oler, H. and Hartmann, U. (1992) "Mapping Neural Network Derived
from the Parzen Window Estimator", Neural Networks, 5, 903-909.

Specht, D.F. (1968) "A practical technique for estimating general
regression surfaces," Lockheed report LMSC 6-79-68-6, Defense Technical

Specht, D.F. (1991) "A Generalized Regression Neural Network", IEEE
Transactions on Neural Networks, 2, Nov. 1991, 568-576.

Wand, M.P., and Jones, M.C. (1995), Kernel Smoothing, London: Chapman &
Hall.

Watson, G.S. (1964) "Smooth regression analysis", Sankhy\=a, Series A,
26, 359-72.

```

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