Afser, HuseyinGyorfi, LaszloWalk, Harro2025-01-062025-01-0620231070-99081558-236110.1109/LSP.2023.33260572-s2.0-85174835698https://doi.org/10.1109/LSP.2023.3326057https://hdl.handle.net/20.500.14669/2069We study the problem of nonparametric classification with repeated observations. Let X be the d dimensional feature vector and let Y denote the label taking values in {1, . . . , M}. In contrast to usual setup with large sample size n and relatively low dimension d, this letter deals with the situation, when instead of observing a single feature vector X we are given t repeated feature vectors V-1, . . . , V-t. Some simple classification rules are presented such that the conditional error probabilities have exponential rate of convergence as t -> infinity.eninfo:eu-repo/semantics/closedAccessClassificationlinear classificationprototype classificationrepeated observationsrobust detectionArticle1526Q1152230WOS:001097101000008Q2