Statistical Classification via Robust Hypothesis Testing

Autor:	Afşer, Hüseyin
Rok vydání:	2021
Předmět:	Computer Science - Information Theory
Druh dokumentu:	Working Paper
Popis:	In this letter, we consider multiple statistical classification problem where a sequence of n independent and identically distributed observations, that are generated by one of M discrete sources, need to be classified. The source distributions are not known, however one has access to labeled training sequences, of length N, from each source. We consider the case where the unknown source distributions are estimated from the training sequences, then the estimates are used as nominal distributions in a robust hypothesis test. Specifically, we consider the robust DGL test due to Devroye et al. and provide non-asymptotic exponential bounds, that are functions of N{n, on the error probability of classification. Comment: Update and revision
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2106.04824 Zobrazit plný text záznamu View this record from Arxiv