The problem of estimating an unknown function from a finite number of noisy data points (examples) is an ill-posed problem of fundamental importance for many applications, such as machine vision, pattern recognition, and process control. Recently, several new computational techniques for non-parametric regression have been proposed by statisticians, and by researchers in artificial neural networks. However, there is little interaction between the two research communities. The goal of this paper is two-fold. First we present a critical survey of statistical and neural network techniques for non-parametric regression. Second, we present comparisons between a representative neural network technique called Constrained Topological Mapping, and several statistical methods, for low-dimensional regression problems. Index Terms-Adaptive Methods, Constrained Topological Mapping, Knot Positioning, MARS, Neural Networks, Projection Pursuit, Regression.
|Original language||English (US)|
|Number of pages||9|
|Journal||Proceedings of SPIE - The International Society for Optical Engineering|
|State||Published - Sep 16 1992|
|Event||Applications of Artificial Neural Networks III 1992 - Orlando, United States|
Duration: Apr 20 1992 → …
Bibliographical noteFunding Information:
The authors would like to thank J.H. Friedman from Stanford for providing MARS code, and for numerous meaningful discussions. This work was supported, in part, by a grant from 3M Corporation.
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