Universal Neyman–Pearson classification with a partially known hypothesis
Parham Boroumand,
Albert Guillén i Fàbregas
Abstract:We propose a universal classifier for binary Neyman–Pearson classification where the 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. In addition, such as Hoeffding’s universal hypothesis test, the proposed classifier is… Show more
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