Learning The Likelihood Test With One-Class Classifiers

arXiv:2210.12494v3 Announce Type: replace
Abstract: Given an observation randomly generated from two alternative probability density functions (pdfs) P0 and P1, we consider the problem of deciding which pdf generated the observation. To design the decision technique we assume that we either know P0 or have a set of samples generated from it; the P1 pdf is instead completely unknown. Such a scenario arises, for example, in security contexts, where the attacker's behavior is completely unknown to the legitimate users. When …

abstract arxiv class classifiers cs.lg decision design eess.sp functions generated likelihood observation pdf pdfs probability samples set stat.ml test type