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Gaussian Process Regression with Mismatched Models | | Peter Sollich
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22 Jun 2001 | | Subject: | Disordered Systems and Neural Networks; Statistical Mechanics | cond-mat.dis-nn cond-mat.stat-mech | | Abstract: | Learning curves for Gaussian process regression are well understood when the `student’ model happens to match the `teacher’ (true data generation process). I derive approximations to the learning curves for the more generic case of mismatched models, and find very rich behaviour: For large input space dimensionality, where the results become exact, there are universal (student-independent) plateaux in the learning curve, with transitions in between that can exhibit arbitrarily many over-fitting maxima. In lower dimensions, plateaux also appear, and the asymptotic decay of the learning curve becomes strongly student-dependent. All predictions are confirmed by simulations. | | Source: | arXiv, cond-mat/0106475 | | Services: | Forum | Review | PDF | Favorites | |
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