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On regularized Radon-Nikodym differentiation
We discuss the problem of estimating Radon-Nikodym derivatives.
General regularization in covariate shift adaptation
Sample reweighting is one of the most widely used methods for correcting the error of least squares learning algorithms in reproducing kernel Hilbert spaces (RKHS), that is caused by future data distributions that are different from the training data distribution.
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