This paper shows that a judicious incremental use of Random Features seems to work as if one were to choose several regularization Thikonov factors. This is interesting: Generalization Properties of Learning with Random Features by Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco
We study the generalization properties of regularized learning with random features in the statistical learning theory framework. We show that optimal learning errors can be achieved with a number of features smaller than the number of examples. As a byproduct, we also show that learning with random features can be seen as a form of regularization, rather than only a way to speed up computations.
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