Statistical Classification via Robust Hypothesis Testing: Non-Asymptotic and Simple Bounds

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dc.authoridAfser, Huseyin/0000-0002-6302-4558
dc.contributor.authorAfser, Huseyin
dc.date.accessioned2025-01-06T17:44:38Z
dc.date.available2025-01-06T17:44:38Z
dc.date.issued2021
dc.description.abstractWe consider Bayesian multiple statistical classification problem in the case where the unknown source distributions are estimated from the labeled training sequences, then the estimates are used as nominal distributions in a robust hypothesis test. Specifically, we employ the DGL test due to Devroye et al. and provide non-asymptotic, exponential upper bounds on the error probability of classification. The proposed upper bounds are simple to evaluate and reveal the effects of the length of the training sequences, the alphabet size and the numbers of hypothesis on the error exponent. The proposed method can also be used for large alphabet sources when the alphabet grows sub-quadratically in the length of the test sequence. The simulations indicate that the performance of the proposed method gets close to that of optimal hypothesis testing as the length of the training sequences increases.
dc.identifier.doi10.1109/LSP.2021.3119230
dc.identifier.endpage2116
dc.identifier.issn1070-9908
dc.identifier.issn1558-2361
dc.identifier.scopus2-s2.0-85117258810
dc.identifier.scopusqualityQ1
dc.identifier.startpage2112
dc.identifier.urihttps://doi.org/10.1109/LSP.2021.3119230
dc.identifier.urihttps://hdl.handle.net/20.500.14669/3130
dc.identifier.volume28
dc.identifier.wosWOS:000714200500009
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIEEE-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Signal Processing Letters
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectTraining
dc.subjectUpper bound
dc.subjectTesting
dc.subjectError probability
dc.subjectBayes methods
dc.subjectComplexity theory
dc.subjectTask analysis
dc.subjectStatistical classification
dc.subjectmultiple hypothesis testing
dc.subjectrobust hypothesis testing
dc.subjectDGL test
dc.titleStatistical Classification via Robust Hypothesis Testing: Non-Asymptotic and Simple Bounds
dc.typeArticle

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