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conference paper
A Kalman filtering algorithm for regularization networks
De Nicolao, G.
•
Ferrari-Trecate, G.
2000
Proceedings of the 2000 American Control Conference
Regularization networks are nonparametric estimators obtained from the application of Tychonov regularization or Bayes estimation to the hypersurface reconstruction problem. With the usual algorithm, the computation of the weights scales as $O(n^3)$ where $n$ is the number of data. In this paper we show that for a class of monodimensional problems, the complexity can be reduced to $O(n)$ by a suitable algorithm based on spectral factorization and Kalman filtering. The procedure applies also to smoothing splines and, in a multidimensional context, to additive regularization networks.
Type
conference paper
Authors
De Nicolao, G.
•
Ferrari-Trecate, G.
Publication date
2000
Published in
Proceedings of the 2000 American Control Conference
Volume
4
Start page
2220
End page
2224
Peer reviewed
REVIEWED
EPFL units
Event name | Event place | Event date |
Chicago, Illinois, USA | 28-30 June | |
Available on Infoscience
January 10, 2017
Use this identifier to reference this record