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.
The work preparation was supported by a grant at the time of an author’s visit to the Technical University of Denmark, Department of Mathematical Modelling, CONNECT group, Lyngby (DK).
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© 2001 Springer-Verlag Wien
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Capobianco, E. (2001). Robust On-Line Statistical Learning. In: Kůrková, V., Neruda, R., Kárný, M., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6230-9_106
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DOI: https://doi.org/10.1007/978-3-7091-6230-9_106
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83651-4
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