The rates of convergence of kernel regression estimates and classification rules

Both nonrecursive and recursive nonparametric regression estimates are studied. The rates of weak and strong convergence of kernel estimates, as well as corresponding multiple classification errors, are derived without assuming the existence of the density of the measurements. An application of the obtained results to a nonparametric Bayes predication is presented.