Title :
A Kalman filtering algorithm for regularization networks
Author :
De Nicolao, G. ; Ferrari-Trecate, G.
Author_Institution :
Dipartimento di Inf. e Sistemistica, Pavia Univ., Italy
Abstract :
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(n3) where n is the number of data. 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
Keywords :
Bayes methods; Kalman filters; computational complexity; filtering theory; nonparametric statistics; Bayes estimation; Kalman filtering algorithm; Tychonov regularization; hypersurface reconstruction problem; monodimensional problems; nonparametric estimators; regularization networks; spectral factorization; Cost function; Electronic mail; Filtering algorithms; H infinity control; Kalman filters; Measurement errors; Neural networks; Neurons; Smoothing methods; Training data;
Conference_Titel :
American Control Conference, 2000. Proceedings of the 2000
Conference_Location :
Chicago, IL
Print_ISBN :
0-7803-5519-9
DOI :
10.1109/ACC.2000.878574
