{
  "id": "cond-mat/0201256",
  "version": "v1",
  "published": "2002-01-15T17:42:12.000Z",
  "updated": "2002-01-15T17:42:12.000Z",
  "title": "Rigorous Bounds to Retarded Learning",
  "authors": [
    "Arnaud Buhot",
    "Mirta B. Gordon",
    "Jean-Pierre Nadal"
  ],
  "comment": "1 page, 1 figure. Comment accepted for publication in Physical Review Letters",
  "doi": "10.1103/PhysRevLett.88.099801",
  "categories": [
    "cond-mat.dis-nn"
  ],
  "abstract": "We show that the lower bound to the critical fraction of data needed to infer (learn) the orientation of the anisotropy axis of a probability distribution, determined by Herschkowitz and Opper [Phys.Rev.Lett. 86, 2174 (2001)], is not always valid. If there is some structure in the data along the anisotropy axis, their analysis is incorrect, and learning is possible with much less data points.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2002-01-15T17:42:12.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "rigorous bounds",
      "retarded learning",
      "anisotropy axis",
      "probability distribution",
      "data points"
    ],
    "tags": [
      "journal article"
    ],
    "publication": {
      "publisher": "APS",
      "journal": "Phys. Rev. Lett."
    },
    "note": {
      "typesetting": "TeX",
      "pages": 1,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}