{
  "id": "1908.00868",
  "version": "v1",
  "published": "2019-08-02T14:08:17.000Z",
  "updated": "2019-08-02T14:08:17.000Z",
  "title": "Machine Learning as Ecology",
  "authors": [
    "Owen Howell",
    "Cui Wenping",
    "Robert Marsland III",
    "Pankaj Mehta"
  ],
  "categories": [
    "cs.LG",
    "cond-mat.stat-mech",
    "stat.ML"
  ],
  "abstract": "Machine learning methods have had spectacular success on numerous problems. Here we show that a prominent class of learning algorithms - including Support Vector Machines (SVMs) -- have a natural interpretation in terms of ecological dynamics. We use these ideas to design new online SVM algorithms that exploit ecological invasions, and benchmark performance using the MNIST dataset. Our work provides a new ecological lens through which we can view statistical learning and opens the possibility of designing ecosystems for machine learning. Supplemental code is found at https://github.com/owenhowell20/EcoSVM.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-08-02T14:08:17.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "support vector machines",
      "online svm algorithms",
      "supplemental code",
      "machine learning methods",
      "spectacular success"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}