Regularization networks: Fast weight calculation via Kalman filtering

Regularization networks are nonparametric estimators obtained from the appl ication 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 re duced to O(n) by a suitable algorithm based on spectral factorization and K alman filtering, Moreover, the procedure applies also to smoothing splines.