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- W2161882927 abstract "We consider the query by multiple example problem where the goal is to identify database samples whose content is similar to a collection of query samples. To assess the similarity we use a relative content density which quantifies the relative concentration of the query distribution to the database distribution. If the database distribution is a mixture of the query distribution and a background distribution then it can be shown that database samples whose relative content density is greater than a particular threshold ρ are more likely to have been generated by the query distribution than the background distribution. We describe an algorithm for predicting samples with relative content density greater than ρ that is computationally efficient and possesses strong performance guarantees. We also show empirical results for applications in computer network monitoring and image segmentation." @default.
- W2161882927 created "2016-06-24" @default.
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- W2161882927 date "2009-01-01" @default.
- W2161882927 modified "2023-09-26" @default.
- W2161882927 title "Density-based similarity measures for content based search" @default.
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- W2161882927 doi "https://doi.org/10.1109/acssc.2009.5469837" @default.
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