Conventional and neural network approaches to regression

Vladimir Cherkassky, Filip Mulier

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


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 languageEnglish (US)
Pages (from-to)840-848
Number of pages9
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - Sep 16 1992
EventApplications of Artificial Neural Networks III 1992 - Orlando, United States
Duration: Apr 20 1992 → …

Bibliographical note

Funding 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.

Publisher Copyright:
© 1992 SPIE. All rights reserved.


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