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 -