Exact Rate of Convergence of k-Nearest-Neighbor Classification Rule

View/ Open
Date
2017-10-16MFO Scientific Program
Research in Pairs 2017Author
Györfi, László
Döring, Maik
Walk, Harro
Metadata
Show full item recordOWP-2017-25
Abstract
A binary classification problem is considered. The excess error probability of the k-nearest neighbor classification rule according to the error probability of the Bayes decision is revisited by a decomposition of the excess error probability into approximation and estimation error. Under a weak margin condition and under a modified Lipschitz condition, tight upper bounds are presented such that one avoids the condition that the feature vector is bounded.