Minimum expected risk probability estimates for nonparametric neighborhood classifiers

We consider the problem of estimating class probabilities for a given feature vector using nonparametric neighborhood methods, such as k-nearest neighbors (k-NN). This paper´s contribution is the application of minimum expected risk estimates for neighborhood learning methods, an analytic formula for the minimum expected risk estimate for weighted k-NN classifiers, and examples showing that the difference can be significant