On the Rate of Convergence of Local Averaging Plug-In Classification Rules Under a Margin Condition

The rates of convergence of plug-in kernel, partitioning, and nearest neighbors classification rules are analyzed. A margin condition, which measures how quickly the a posteriori probabilities cross the decision boundary, smoothness conditions on the a posteriori probabilities, and boundedness of the feature vector are imposed. The rates of convergence of the plug-in classifiers shown in this paper are faster than previously known