Top Document: comp.ai.neural-nets FAQ, Part 2 of 7: Learning Previous Document: What is PNN? Next Document: What does unsupervised learning learn? See reader questions & answers on this topic! - Help others by sharing your knowledge 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; gradient descent is not used. 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 Information Center AD-672505. 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. User Contributions:Top Document: comp.ai.neural-nets FAQ, Part 2 of 7: Learning Previous Document: What is PNN? Next Document: What does unsupervised learning learn? Part1 - Part2 - Part3 - Part4 - Part5 - Part6 - Part7 - Single Page [ Usenet FAQs | Web FAQs | Documents | RFC Index ] Send corrections/additions to the FAQ Maintainer: saswss@unx.sas.com (Warren Sarle)
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