Regularization networks: fast weight calculation via Kalman filtering

Author

De Nicolao, Giuseppe ; Ferrari-Trecate, Giancarlo

Author_Institution

Dipartimento di Inf. e Sistemistica, Pavia Univ., Italy

Volume

12

Issue

2

fYear

2001

fDate

3/1/2001 12:00:00 AM

Firstpage

228

Lastpage

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 back is that the computation of the weights scales as O(n³) 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

Keywords

Bayes methods; Kalman filters; computational complexity; learning (artificial intelligence); radial basis function networks; smoothing methods; splines (mathematics); stochastic processes; Bayes estimation; Kalman filtering; computational complexity; learning; radial basis function neural nets; regularization networks; smoothing splines; spectral factorization; stochastic processes; Costs; Filtering algorithms; Helium; Kalman filters; Neural networks; Neurons; Radial basis function networks; Smoothing methods; Stochastic processes; Training data;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.914520

Filename

914520