On the Rate of Convergence of Local Averaging Plug-In Classification Rules Under a Margin Condition
Michael C. Kohler, Adam Krzyżak · IEEE Transactions on Information Theory · 2007
The rates of convergence of plug-in kernel, partitioning, and nearest neighbors classification rules are analyzed. A margin condition, which measures how quickly thea posterioriprobabilities cross the decision boundary, smoothness conditions on thea posterioriprobabilities, 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.