{
  "id": "2312.13842",
  "version": "v1",
  "published": "2023-12-21T13:40:31.000Z",
  "updated": "2023-12-21T13:40:31.000Z",
  "title": "Statistical learning theory and Occam's razor: The argument from empirical risk minimization",
  "authors": [
    "Tom F. Sterkenburg"
  ],
  "categories": [
    "cs.LG",
    "math.ST",
    "stat.TH"
  ],
  "abstract": "This paper considers the epistemic justification for a simplicity preference in inductive inference that may be obtained from the machine learning framework of statistical learning theory. Uniting elements from both earlier arguments suggesting and rejecting such a justification, the paper spells out a qualified means-ends and model-relative justificatory argument, built on statistical learning theory's central mathematical learning guarantee for the method of empirical risk minimization.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-12-21T13:40:31.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "statistical learning theory",
      "empirical risk minimization",
      "occams razor",
      "central mathematical learning guarantee",
      "theorys central mathematical learning"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}