On different facets of regularization theory
Neural Computation
Nonparametric identification of population models via Gaussian processes
Automatica (Journal of IFAC)
Bayes and empirical Bayes semi-blind deconvolution using eigenfunctions of a prior covariance
Automatica (Journal of IFAC)
Wavelet estimation by Bayesian thresholding and model selection
Automatica (Journal of IFAC)
Brief paper: Fast computation of smoothing splines subject to equality constraints
Automatica (Journal of IFAC)
Prediction error identification of linear systems: A nonparametric Gaussian regression approach
Automatica (Journal of IFAC)
Use of input deformations with brownian motion filters for discontinuous regression
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Brief Regularization networks for inverse problems: A state-space approach
Automatica (Journal of IFAC)
Consistent identification of Wiener systems: A machine learning viewpoint
Automatica (Journal of IFAC)
| Hi-index | 0.02 |
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