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- W4385430012 abstract "Given n samples of a function f:D→ℂ in random points drawn with respect to a measure ϱ S we develop theoretical analysis of the L 2 (D,ϱ T )-approximation error. For a parituclar choice of ϱ S depending on ϱ T , it is known that the weighted least squares method from finite dimensional function spaces V m , dim(V m )=m<∞ has the same error as the best approximation in V m up to a multiplicative constant when given exact samples with logarithmic oversampling. If the source measure ϱ S and the target measure ϱ T differ we are in the domain adaptation setting, a subfield of transfer learning. We model the resulting deterioration of the error in our bounds." @default.
- W4385430012 created "2023-08-01" @default.
- W4385430012 creator A5062725379 @default.
- W4385430012 date "2023-07-31" @default.
- W4385430012 modified "2023-10-10" @default.
- W4385430012 title "Error Guarantees for Least Squares Approximation with Noisy Samples in Domain Adaptation" @default.
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- W4385430012 doi "https://doi.org/10.5802/smai-jcm.96" @default.
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