Open access
Date
2024-03-06Type
- Conference Paper
ETH Bibliography
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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000664580Publication status
publishedBook title
International Zurich Seminar on Information and Communication (IZS 2024). ProceedingsPages / Article No.
Publisher
ETH ZurichEvent
Organisational unit
02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.
Related publications and datasets
Is part of: https://doi.org/10.3929/ethz-b-000664209
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ETH Bibliography
no
Altmetrics