G. Lugosi et M. Pawlak, ON THE POSTERIOR-PROBABILITY ESTIMATE OF THE ERROR RATE OF NONPARAMETRIC CLASSIFICATION RULES, IEEE transactions on information theory, 40(2), 1994, pp. 475-481
Citations number
33
Categorie Soggetti
Information Science & Library Science","Engineering, Eletrical & Electronic
The posterior-probability estimate of the classification error rate of
some nonparametric classification rules is studied. The variance of t
he estimator is shown to have some remarkable distribution-free proper
ties for the k-nearest neighbor, kernel, and histogram rules. We also
investigate the bias of the estimate and establish its consistency and
upper bounds. The version of the estimate calculated from an independ
ent set of unclassified patterns is also considered.