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EnglishRegularization 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.
Regularization networks: Fast weight calculation via Kalman filtering / DE NICOLAO G.; FERRARI TRECATE G.. - In: IEEE TRANSACTIONS ON NEURAL NETWORKS. - ISSN 1045-9227. - STAMPA. - 12:2(2001), pp. 228-235.
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| Titolo: | Regularization networks: Fast weight calculation via Kalman filtering |
| Autori: | |
| Data di pubblicazione: | 2001 |
| Rivista: | |
| Citazione: | Regularization networks: Fast weight calculation via Kalman filtering / DE NICOLAO G.; FERRARI TRECATE G.. - In: IEEE TRANSACTIONS ON NEURAL NETWORKS. - ISSN 1045-9227. - STAMPA. - 12:2(2001), pp. 228-235. |
| Abstract: | 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. |
| Handle: | http://hdl.handle.net/11571/107875 |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
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