Regularization networks: Fast weight calculation via Kalman filtering

被引：21

作者：

De Nicolao, G ^{[1
]}

Ferrari-Trecate, G ^{[1
]}

机构：

[1] Univ Pavia, Dipartimento Informat & Sistemist, I-27100 Pavia, Italy

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2001年 / 12卷 / 02期

基金：

美国国家卫生研究院;

关键词：

Bayesian estimation; Kalman filter; RBF networks; regularization; smoothing splines; stochastic processes;

D O I：

10.1109/72.914520

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Regularization networks are nonparametric estimators obtained from the application 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 reduced to O(n) by a suitable algorithm based on spectral factorization and Kalman filtering, Moreover, the procedure applies also to smoothing splines.

引用

页码：228 / 235

页数：8

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