Smoothing noisy data with spline functions

By Michael F. Hutchinson, F. Hoog. 1985

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1. James O. Ramsay
2. J. Urgen Schmidhuber
3. Nancy E. Heckman
4. David R Ha
5. Yoshua Bengio
6. Sergey Levine
7. Quoc V. Le
8. Chelsea Finn

Citations in the corpus

• Deep learning in neural networks: An overview (2014, methodology): “Often weight priors are implicit in additional penalty terms (MacKay, 1992) or in methods based on validation sets (Craven & Wahba, 1979; Eubank, 1988; Golub, Heath, & Wahba, 1979; Hastie & Tibshirani, 1990; Mosteller & Tukey, 1968; Stone, 1974), Akaike’s information criterion and final prediction…”
• Penalized regression with model‐based penalties (2000, methodology): “In defense of the Kalman-lter approach, however, Kohn & Ansley (1993) found that, for the L = D 2 case, the approach is at least competitive with the widely available cubic smoothing spline method of Reinsch (1967, 1971) and Hutchison & de Hoog (1985) .”