{
  "id": "1902.04622",
  "version": "v1",
  "published": "2019-02-12T20:28:09.000Z",
  "updated": "2019-02-12T20:28:09.000Z",
  "title": "Learning Theory and Support Vector Machines - a primer",
  "authors": [
    "Michael Banf"
  ],
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "The main goal of statistical learning theory is to provide a fundamental framework for the problem of decision making and model construction based on sets of data. Here, we present a brief introduction to the fundamentals of statistical learning theory, in particular the difference between empirical and structural risk minimization, including one of its most prominent implementations, i.e. the Support Vector Machine.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-02-12T20:28:09.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "support vector machine",
      "statistical learning theory",
      "structural risk minimization",
      "fundamental framework",
      "model construction"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}