Classification With Repeated Observations
Hüseyin Afşer, László Györfi, Harro Walk · IEEE Signal Processing Letters · 2023
We study the problem of nonparametric classification with repeated observations. Let${\mathbf {X}}$be the$d$dimensional feature vector and let$Y$denote the label taking values in$\lbrace 1,\ldots, M\rbrace$. 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${\mathbf {X}}$we are given$t$repeated feature vectors${\mathbf {V}}_{1},\ldots, {\mathbf {V}}_{t}$. Some simple classification rules are presented such that the conditional error probabilities have exponential rate of convergence as$t\to \infty$.