Conventional and neural network approaches to regression

Vladimir Cherkassky; Filip M. Mulier

doi:10.1117/12.140069

16 September 1992 Conventional and neural network approaches to regression

Vladimir Cherkassky, Filip M. Mulier

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.140069
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

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 twofold. 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.

Citation Download Citation

Vladimir Cherkassky and Filip M. Mulier "Conventional and neural network approaches to regression", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.140069

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