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.
rate of convergence classification plug-in classifiers statistical learning