Asymptotic analysis of the learning curve for Gaussian process regression
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| 024 | 7 | 0 | |a 10.1007/s10994-014-5437-0 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-014-5437-0 | ||
| 245 | 0 | 0 | |a Asymptotic analysis of the learning curve for Gaussian process regression |h [Elektronische Daten] |c [Loic Le Gratiet, Josselin Garnier] |
| 520 | 3 | |a This paper deals with the learning curve in a Gaussian process regression framework. The learning curve describes the generalization error of the Gaussian process used for the regression. The main result is the proof of a theorem giving the generalization error for a large class of correlation kernels and for any dimension when the number of observations is large. From this theorem, we can deduce the asymptotic behavior of the generalization error when the observation error is small. The presented proof generalizes previous ones that were limited to special kernels or to small dimensions (one or two). The theoretical results are applied to a nuclear safety problem. | |
| 540 | |a The Author(s), 2014 | ||
| 690 | 7 | |a Gaussian process regression |2 nationallicence | |
| 690 | 7 | |a Asymptotic mean squared error |2 nationallicence | |
| 690 | 7 | |a Learning curves |2 nationallicence | |
| 690 | 7 | |a Generalization error |2 nationallicence | |
| 690 | 7 | |a Convergence rate |2 nationallicence | |
| 700 | 1 | |a Le Gratiet |D Loic |u Université Paris Diderot, 75205, Paris Cedex 13, France |4 aut | |
| 700 | 1 | |a Garnier |D Josselin |u Laboratoire de Probabilites et Modeles Aleatoires & Laboratoire Jacques-Louis Lions, Universite Paris Diderot, 75205, Paris Cedex 13, France |4 aut | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 98/3(2015-03-01), 407-433 |x 0885-6125 |q 98:3<407 |1 2015 |2 98 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-014-5437-0 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10994-014-5437-0 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Le Gratiet |D Loic |u Université Paris Diderot, 75205, Paris Cedex 13, France |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Garnier |D Josselin |u Laboratoire de Probabilites et Modeles Aleatoires & Laboratoire Jacques-Louis Lions, Universite Paris Diderot, 75205, Paris Cedex 13, France |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Machine Learning |d Springer US; http://www.springer-ny.com |g 98/3(2015-03-01), 407-433 |x 0885-6125 |q 98:3<407 |1 2015 |2 98 |o 10994 | ||