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Open Access

Robust Control Methods for On-Line Statistical Learning

EURASIP Journal on Advances in Signal Processing20012001:287964

Received: 8 January 2001

Published: 1 June 2001


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.


artificial learningstatistical control algorithmsrobustness and efficiency of estimatorsmaximum likelihood inference

Authors’ Affiliations

CWI, Amsterdam, The Netherlands


© Capobianco 2001