Repeated Observations for Classification

Hüseyin Afşer; László Györfi; Harro Walk

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arXiv · 2307.09896

Repeated Observations for Classification

Hüseyin Afşer·László Györfi·Harro Walk

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Abstract

We study the problem nonparametric classification with repeated observations. Let $\bX$ be the $d$ dimensional feature vector and let $Y$ denote the label taking values in $\{1,\dots ,M\}$. In contrast to usual setup with large sample size $n$ and relatively low dimension $d$, this paper deals with the situation, when instead of observing a single feature vector $\bX$ we are given $t$ repeated feature vectors $\bV_1,\dots ,\bV_t $. Some simple classification rules are presented such that the conditional error probabilities have exponential convergence rate of convergence as $t\to\infty$. In the analysis, we investigate particular models like robust detection by nominal densities, prototype classification, linear transformation, linear classification, scaling.

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BibTeX RIS

Hüseyin Afşer, László Györfi, Harro Walk. 2023-07-19. Repeated Observations for Classification. https://arxiv.org/abs/2307.09896

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