{
  "id": "1602.04474",
  "version": "v1",
  "published": "2016-02-14T16:26:39.000Z",
  "updated": "2016-02-14T16:26:39.000Z",
  "title": "Generalization Properties of Learning with Random Features",
  "authors": [
    "Alessandro Rudi",
    "Raffaello Camoriano",
    "Lorenzo Rosasco"
  ],
  "categories": [
    "stat.ML",
    "cs.LG"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2016-02-14T16:26:39.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "random features",
      "generalization properties",
      "features smaller",
      "optimal learning errors",
      "statistical learning theory framework"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}