Nonparametric kernel algorithm for recovery of functions from noisy measurements with applications

This note presents a kernel algorithm for recovery of a regression function from noisy data. Conditions are provided that assure pointwise convergence in the mean square and almost sure senses. An application to a class of linear system identification problems is discussed.