Related Experiment Video
Updated: Jul 7, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
Published on: March 1, 2022
Maximum likelihood neural approximation in presence of additive colored noise
1Lab. des Images et des Signaux, CNRS, Grenoble.
Abstract:
In many practical situations, the noise samples may be correlated. In this case, the estimation of noise parameters can be used to improve the approximation. Estimation of the noise structure can also be used to find a stopping criterion in constructive neural networks. To avoid overfitting, a network construction procedure must be stopped when residual can be considered as noise. The knowledge on the noise may be used for "whitening" the residual so that a correlation hypothesis test determines if the network growing must be continued or not. In this paper, supposing a Gaussian noise model, we study the problem of multi-output nonlinear regression using MLP when the noise in each output is a correlated autoregressive time series and is spatially correlated with other output noises. We show that the noise parameters can be determined simultaneously with the network weights and used to construct an estimator with a smaller variance, and so to improve the network generalization performance. Moreover, if a constructive procedure is used to build the network, the estimated parameters may be used to stop the procedure.
Related Concept Videos
Application of Linearization and Approximation
Linear Approximation in Frequency Domain
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Linear Approximations
Linear Approximation in Time Domain
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Linearization and Approximation
Propagation of Uncertainty from Random Error