Distance-Penalized Active Learning Using Quantile Search

28 Sep 2015John LiporBrandon WongDonald ScaviaBranko KerkezLaura Balzano

Adaptive sampling theory has shown that, with proper assumptions on the signal class, algorithms exist to reconstruct a signal in $\mathbb{R}^{d}$ with an optimal number of samples. We generalize this problem to the case of spatial signals, where the sampling cost is a function of both the number of samples taken and the distance traveled during estimation... (read more)

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