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An Incremental Multivariate Regression Method
for Function Approximation from Noisy Data

M. Carozza and S. Rampone
Universit del Sannio

Abstract
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.

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