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- W4385890363 abstract "We discuss the problem of estimating Radon-Nikodym derivatives. This problem appears in various applications, such as covariate shift adaptation, likelihood-ratio testing, mutual information estimation, and conditional probability estimation. To address the above problem, we employ the general regularization scheme in reproducing kernel Hilbert spaces. The convergence rate of the corresponding regularized algorithm is established by taking into account both the smoothness of the derivative and the capacity of the space in which it is estimated. This is done in terms of general source conditions and the regularized Christoffel functions. We also find that the reconstruction of Radon-Nikodym derivatives at any particular point can be done with high order of accuracy. Our theoretical results are illustrated by numerical simulations." @default.
- W4385890363 created "2023-08-17" @default.
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- W4385890363 date "2023-08-15" @default.
- W4385890363 modified "2023-09-27" @default.
- W4385890363 title "On regularized Radon-Nikodym differentiation" @default.
- W4385890363 doi "https://doi.org/10.48550/arxiv.2308.07887" @default.
- W4385890363 hasPublicationYear "2023" @default.
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