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- W1505709231 abstract "AbstractThe use of non-orthonormal basis functions in ridge regression leads to an often undesired non-isotropic prior in function space. In this study, we investigate an alternative regularization technique that results in an implicit whitening of the basis functions by penalizing directions in function space with a large prior variance. The regularization term is computed from unlabelled input data that characterizes the input distribution. Tests on two datasets using polynomial basis functions showed an improved average performance compared to standard ridge regression.KeywordsFunction SpaceUnlabelled DataRidge RegressionReproduce Kernel Hilbert SpaceInterval BoundaryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W1505709231 date "2004-01-01" @default.
- W1505709231 modified "2023-09-23" @default.
- W1505709231 title "Semi-supervised Kernel Regression Using Whitened Function Classes" @default.
- W1505709231 cites W2029469881 @default.
- W1505709231 doi "https://doi.org/10.1007/978-3-540-28649-3_3" @default.
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