Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco
Published 2016-02-14Version 1
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.
Generalization Properties of Decision Trees on Real-valued and Categorical Features
Learning with invariances in random features and kernel models
On the Generalization Properties of Adversarial Training
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