{
  "id": "1103.1790",
  "version": "v1",
  "published": "2011-03-09T14:28:39.000Z",
  "updated": "2011-03-09T14:28:39.000Z",
  "title": "Rates of convergence in active learning",
  "authors": [
    "Steve Hanneke"
  ],
  "comment": "Published in at http://dx.doi.org/10.1214/10-AOS843 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)",
  "journal": "Annals of Statistics 2011, Vol. 39, No. 1, 333-361",
  "doi": "10.1214/10-AOS843",
  "categories": [
    "math.ST",
    "stat.TH"
  ],
  "abstract": "We study the rates of convergence in generalization error achievable by active learning under various types of label noise. Additionally, we study the general problem of model selection for active learning with a nested hierarchy of hypothesis classes and propose an algorithm whose error rate provably converges to the best achievable error among classifiers in the hierarchy at a rate adaptive to both the complexity of the optimal classifier and the noise conditions. In particular, we state sufficient conditions for these rates to be dramatically faster than those achievable by passive learning.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2011-03-09T14:28:39.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "active learning",
      "convergence",
      "error rate provably converges",
      "state sufficient conditions",
      "general problem"
    ],
    "tags": [
      "journal article"
    ],
    "publication": {
      "publisher": "Institute of Mathematical Statistics",
      "journal": "Ann. Stat."
    },
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2011arXiv1103.1790H"
    }
  }
}