Open access
Datum
2024-03-06Typ
- Conference Paper
ETH Bibliographie
no
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Abstract
We propose a universal classifier for binary Neyman–Pearson classification where null distribution is known while only a training sequence is available for the alternative distribution. The proposed classifier interpolates between Hoeffding’s classifier and the likelihood ratio test and attains the same error probability prefactor as the likelihood ratio test, i.e., the same prefactor as if both distributions were known. Similarly to Hoeffding’s universal hypothesis test, the proposed classifier is shown to attain the optimal error exponent tradeoff attained by the likelihood ratio test whenever the ratio of training to observation samples exceeds a certain value. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000664580Publikationsstatus
publishedBuchtitel
International Zurich Seminar on Information and Communication (IZS 2024). ProceedingsSeiten / Artikelnummer
Verlag
ETH ZurichKonferenz
Organisationseinheit
02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.
Zugehörige Publikationen und Daten
Is part of: https://doi.org/10.3929/ethz-b-000664209
ETH Bibliographie
no
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