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Robust Control Methods for On-Line Statistical Learning

Author(s): Capobianco Enrico

Journal: EURASIP Journal on Advances in Signal Processing
ISSN 1687-6172

Volume: 2001;
Issue: 2;
Start page: 287964;
Date: 2001;
Original page

Keywords: artificial learning | statistical control algorithms | robustness and efficiency of estimators | maximum likelihood inference

The issue of controlling that data processing in an experiment results not affected by the presence of outliers is relevant for statistical control and learning studies. Learning schemes should thus be tested for their capacity of handling outliers in the observed training set so to achieve reliable estimates with respect to the crucial bias and variance aspects. We describe possible ways of endowing neural networks with statistically robust properties by defining feasible error criteria. It is convenient to cast neural nets in state space representations and apply both Kalman filter and stochastic approximation procedures in order to suggest statistically robustified solutions for on-line learning.
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