Title :
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
fDate :
3/1/2001 12:00:00 AM
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;
Journal_Title :
Neural Networks, IEEE Transactions on
