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It is shown that the kernel estimate of the regression is weakly or strongly consistent for almost all , where is the probability measure of . The result is valid for any distribution of . The asymptotical optimality of classification rules derived from the estimate is examined. The optimality is independent of class distributions, i.e., it is distribution-free.