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We study a general class of statistical detection problems where the underlying objective is to detect items belonging to a rare class from a very large database. We propose a computationally efficient method to achieve this goal. Our method consists of two steps. In the first step we estimate the density function of the rare class alone with an adaptive bandwidth kernel density estimator. The adaptive choice of the bandwidth is inspired by the ancient Chinese board game known today as Go. Indoi:10.1198/004017005000000643 fatcat:qs2hobjudvfhzm5kvxdsk6lyuy