Publication
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 where is the number of data. In this paper we show that for a class of monodimensional problems, the complexity can be reduced to 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.
Colin Neil Jones, Yingzhao Lian, Jicheng Shi
Tatiana Pieloni, Ekaterina Krymova, Loïc Thomas Davies Coyle, Michael Schenk