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How isotropic kernels perform on simple invariants
[article]
2020
arXiv
pre-print
We investigate how the training curve of isotropic kernel methods depends on the symmetry of the task to be learned, in several settings. (i) We consider a regression task, where the target function is a Gaussian random field that depends only on d_∥ variables, fewer than the input dimension d. We compute the expected test error ϵ that follows ϵ∼ p^-β where p is the size of the training set. We find that β∼ 1/d independently of d_∥, supporting previous findings that the presence of invariants
arXiv:2006.09754v5
fatcat:zh6n4tjzkbdprohbrojpoi2btu