Neural networks and nonparametric regression

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The problem of estimating an unknown function from a finite number of noisy data points is a problem of fundamental importance for many applications in signal processing, machine vision, pattern recognition and process control. Recently, several new computational techniques for non-parametric regression have been proposed by the statisticians and by researchers in artificial neural networks. This paper presents a critical survey and a common taxonomy of statistical and neural network methods for regression.

Original languageEnglish (US)
Title of host publicationNeural Networks for Signal Processing II - Proceedings of the 1992 IEEE Workshop
EditorsC.A. Kamm, S.Y. Kung, J. Aa. Sorenson, F. Fallside
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages511-521
Number of pages11
ISBN (Electronic)0780305574
DOIs
StatePublished - Jan 1 1992
Event1992 IEEE Workshop on Neural Networks for Signal Processing II - Helsingoer, Denmark
Duration: Aug 31 1992Sep 2 1992

Publication series

NameNeural Networks for Signal Processing - Proceedings of the IEEE Workshop

Other

Other1992 IEEE Workshop on Neural Networks for Signal Processing II
CountryDenmark
CityHelsingoer
Period8/31/929/2/92

Fingerprint Dive into the research topics of 'Neural networks and nonparametric regression'. Together they form a unique fingerprint.

Cite this