{
  "id": "1212.4457",
  "version": "v2",
  "published": "2012-12-18T18:54:45.000Z",
  "updated": "2018-01-29T18:28:23.000Z",
  "title": "Probability bounds for active learning in the regression problem",
  "authors": [
    "Ana Karina Fermin",
    "Carenne Ludeña"
  ],
  "categories": [
    "math.ST",
    "stat.TH"
  ],
  "abstract": "In this article we consider the problem of choosing an optimal sampling scheme for the regression problem simultaneously with that of model selection. We consider a batch type approach and an on-line approach following algorithms recently developed for the classification problem. Our main tools are concentration-type inequalities which allow us to bound the supremum of the deviations of the sampling scheme corrected by an appropriate weight function.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2012-12-18T18:54:45.000Z",
      "comment": null,
      "journal": null,
      "doi": null
    },
    {
      "version": "v2",
      "updated": "2018-01-29T18:28:23.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "68T05",
      "62J05",
      "62K05"
    ],
    "keywords": [
      "regression problem",
      "probability bounds",
      "active learning",
      "appropriate weight function",
      "batch type approach"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2012arXiv1212.4457F"
    }
  }
}