Note: This content is accessible to all versions of every browser. However, this browser does not seem to support current Web standards, preventing the display of our site's design details.

  

Regularization Networks: Fast Weight Calculation via Kalman Filtering

Author(s):

G. De Nicolao, G. Ferrari-Trecate
Conference/Journal:

IEEE Trans. on Neural Networks, vol. 12, no. 2, pp. 228-235
Abstract:

Regularization networks are nonparametric estimators obtained from the application of Tychonov regularization or Bayes estimation to the hypersurface reconstruction problem. Their main drawback is that 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. Moreover, the procedure applies also to smoothing splines.

Year:

2001
Type of Publication:

(01)Article
Supervisor:



File Download:

Request a copy of this publication.
(Uses JavaScript)
% Autogenerated BibTeX entry
@Article { NicFer:2001:IFA_1748,
    author={G. De Nicolao and G. Ferrari-Trecate},
    title={{Regularization Networks: Fast Weight Calculation via Kalman
	  Filtering}},
    journal={IEEE Trans. on Neural Networks},
    year={2001},
    volume={12},
    number={2},
    pages={228--235},
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=1748}
}
Permanent link