HAL (France)
open access
A deterministic analysis of a class of nonlinear regression methods
We consider the problem of learning a general class of nonlinear predictors from noisy data. In this context a class of optimal estimators is formed and analyzed in term of performance and convergence. More precisely, we derive bounds on the estimation errors which depend quantitatively on the compl
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