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Mapping for Approximation: A Unified View of Rescaled, Variably Scaled and Rational Kernel Methods
Classical approximation methods are usually improved by changing the sampling set, increasing the number of data points, or selecting a different basis. We advocate a complementary viewpoint: keep the sampled values fixed and modify the representation through a suitable mapping. This viewpoint unifi
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