{
  "id": "cs/0402020",
  "version": "v1",
  "published": "2004-02-11T16:34:16.000Z",
  "updated": "2004-02-11T16:34:16.000Z",
  "title": "Geometrical Complexity of Classification Problems",
  "authors": [
    "Tin Kam Ho"
  ],
  "comment": "Proceedings of the 7th Course on Ensemble Methods for Learning Machines at the International School on Neural Nets ``E.R. Caianiello''",
  "categories": [
    "cs.CV"
  ],
  "abstract": "Despite encouraging recent progresses in ensemble approaches, classification methods seem to have reached a plateau in development. Further advances depend on a better understanding of geometrical and topological characteristics of point sets in high-dimensional spaces, the preservation of such characteristics under feature transformations and sampling processes, and their interaction with geometrical models used in classifiers. We discuss an attempt to measure such properties from data sets and relate them to classifier accuracies.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2004-02-11T16:34:16.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "I.5.0"
    ],
    "keywords": [
      "classification problems",
      "geometrical complexity",
      "classifier accuracies",
      "data sets",
      "feature transformations"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2004cs........2020H"
    }
  }
}