Exact rate of convergence of kernel-based classification rule

Maik Döring, László Györfi, Harro Walk

Research output: Contribution to journalArticle


A binary classification problem is considered, where the posteriori probability is estimated by the nonparametric kernel regression estimate with naive kernel. The excess error probability of the corresponding plug-in decision classification rule according to the error probability of the Bayes decision is studied such that the excess error probability is decomposed into approximation and estimation error. A general formula is derived for the approximation error. Under a weak margin condition and various smoothness conditions, tight upper bounds are presented on the approximation error. By a Berry-Esseen type central limit theorem a general expression for the estimation error is shown.

Original languageEnglish
Pages (from-to)71-91
Number of pages21
JournalStudies in Computational Intelligence
Publication statusPublished - Jan 1 2016


  • Classification error probability
  • Kernel rule
  • Lower bound
  • Margin condition
  • Upper bound

ASJC Scopus subject areas

  • Artificial Intelligence

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