{
  "id": "1905.00331",
  "version": "v1",
  "published": "2019-05-01T14:43:03.000Z",
  "updated": "2019-05-01T14:43:03.000Z",
  "title": "High-Performance Support Vector Machines and Its Applications",
  "authors": [
    "Taiping He",
    "Tao Wang",
    "Ralph Abbey",
    "Joshua Griffin"
  ],
  "comment": "ICDATA 2018",
  "categories": [
    "cs.LG",
    "cs.DC",
    "stat.ML"
  ],
  "abstract": "The support vector machines (SVM) algorithm is a popular classification technique in data mining and machine learning. In this paper, we propose a distributed SVM algorithm and demonstrate its use in a number of applications. The algorithm is named high-performance support vector machines (HPSVM). The major contribution of HPSVM is two-fold. First, HPSVM provides a new way to distribute computations to the machines in the cloud without shuffling the data. Second, HPSVM minimizes the inter-machine communications in order to maximize the performance. We apply HPSVM to some real-world classification problems and compare it with the state-of-the-art SVM technique implemented in R on several public data sets. HPSVM achieves similar or better results.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-05-01T14:43:03.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "applications",
      "named high-performance support vector machines",
      "public data sets",
      "state-of-the-art svm technique",
      "real-world classification problems"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}