TY - GEN

T1 - Neural networks and nonparametric regression

AU - Cherkassky, Vladimir

PY - 1992/1/1

Y1 - 1992/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=33747772325&partnerID=8YFLogxK

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U2 - 10.1109/NNSP.1992.253661

DO - 10.1109/NNSP.1992.253661

M3 - Conference contribution

AN - SCOPUS:33747772325

T3 - Neural Networks for Signal Processing - Proceedings of the IEEE Workshop

SP - 511

EP - 521

BT - Neural Networks for Signal Processing II - Proceedings of the 1992 IEEE Workshop

A2 - Kamm, C.A.

A2 - Kung, S.Y.

A2 - Sorenson, J. Aa.

A2 - Fallside, F.

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 1992 IEEE Workshop on Neural Networks for Signal Processing II

Y2 - 31 August 1992 through 2 September 1992

ER -