{
  "id": "0902.3453",
  "version": "v1",
  "published": "2009-02-19T20:50:53.000Z",
  "updated": "2009-02-19T20:50:53.000Z",
  "title": "Escaping the curse of dimensionality with a tree-based regressor",
  "authors": [
    "Samory Kpotufe"
  ],
  "categories": [
    "stat.ML"
  ],
  "abstract": "We present the first tree-based regressor whose convergence rate depends only on the intrinsic dimension of the data, namely its Assouad dimension. The regressor uses the RPtree partitioning procedure, a simple randomized variant of k-d trees.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2009-02-19T20:50:53.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "dimensionality",
      "convergence rate depends",
      "intrinsic dimension",
      "first tree-based regressor",
      "simple randomized variant"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2009arXiv0902.3453K"
    }
  }
}