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Robust On-Line Statistical Learning

  • Enrico Capobianco
Conference paper

Abstract

We describe possible ways of endowing neural networks with statistically robust properties. We especially look at learning schemes resistant to outliers by defining error criteria able to handle deviations from convenient probability distribution assumptions. It comes out to be 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.

Keywords

Prediction Error Kalman Filter Influence Function State Space Representation Kalman Filter Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • Enrico Capobianco
    • 1
  1. 1.CNR — Consiglio Nazionale delle RicercheItaly

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