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An incremental multivariate regression method for function approximation from noisy data

delete2001-03-01
delete6
PRE
AI
M
Menita Carozza
S
Salvatore Rampone
DOI：10.1016/S0031-3203(00)00020-0delete
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Abstract

Abstract

En 中文
In this paper we consider the problem of approximating functions from noisy data. We propose an incremental supervised learning algorithm for RBF networks. Hidden Gaussian nodes are added in an iterative manner during the training process. For each new node added, the activation function center and the output connection weight are settled according to an extended chained version of the Nadaraja-Watson estimator. Then the variances of the activation functions are determined by an empirical risk-driven rule based on a genetic-like optimization technique. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Keywords:
function approximation
noisy data
network size
genetic algorithm
generalization
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Journal

Pattern Recognition cover
Pattern Recognition
IF:
7.6
Papers:
1.3W
Citations:
4.5W

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