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刊物类别:Computer Science
刊物主题:Artificial Intelligence and Robotics Automation and Robotics Computing Methodologies Simulation and Modeling Language Translation and Linguistics
出版者:Springer Netherlands
ISSN:1573-0565
文摘
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.