{
  "id": "cond-mat/9611027",
  "version": "v1",
  "published": "1996-11-05T19:06:21.000Z",
  "updated": "1996-11-05T19:06:21.000Z",
  "title": "Finite size scaling in neural networks",
  "authors": [
    "Walter Nadler",
    "Wolfgang Fink"
  ],
  "comment": "4 pages, RevTex, 5 figures, uses multicol.sty and psfig.sty",
  "doi": "10.1103/PhysRevLett.78.555",
  "categories": [
    "cond-mat.dis-nn",
    "adap-org",
    "nlin.AO"
  ],
  "abstract": "We demonstrate that the fraction of pattern sets that can be stored in single- and hidden-layer perceptrons exhibits finite size scaling. This feature allows to estimate the critical storage capacity \\alpha_c from simulations of relatively small systems. We illustrate this approach by determining \\alpha_c, together with the finite size scaling exponent \\nu, for storing Gaussian patterns in committee and parity machines with binary couplings and up to K=5 hidden units.",
  "revisions": [
    {
      "version": "v1",
      "updated": "1996-11-05T19:06:21.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "neural networks",
      "critical storage capacity",
      "hidden units",
      "hidden-layer perceptrons",
      "relatively small systems"
    ],
    "tags": [
      "journal article"
    ],
    "publication": {
      "publisher": "APS",
      "journal": "Phys. Rev. Lett."
    },
    "note": {
      "typesetting": "RevTeX",
      "pages": 4,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}