rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Ensemble methods
    sub title: foundations and algorithms
  name:
    name part: Zhou, Zhi-Hua
    role:
      role term: creator
    role:
      role term: author
  type of resource: text
  origin info:
    place:
      place term: flu
    place:
      place term: Boca Raton, FL
    publisher: Taylor & Francis
    date issued: 2012
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: xiv, 222 p. : ill. ; 25 cm.
  abstract: "This comprehensive book presents an in-depth and systematic introduction to ensemble methods for researchers in machine learning, data mining, and related areas. It helps readers solve modem problems in machine learning using these methods. The author covers the spectrum of research in ensemble methods, including such famous methods as boosting, bagging, and rainforest, along with current directions and methods not sufficiently addressed in other books. Chapters explore cutting-edge topics, such as semi-supervised ensembles, cluster ensembles, and comprehensibility, as well as successful applications"-- Provided by publisher
  table of contents: 1. Introduction -- 2. Boosting -- 3. Bagging -- 4. Combination methods -- 5. Diversity -- 6. Ensemble pruning -- 7. Clustering Ensemble -- 8. Advanced Topics
  note: Zhi-Hua Zhou
  note: Includes bibliographical references (p. 187-218) and index
  subject:
    topic: Mathematical analysis
  subject:
    topic: Multiple comparisons (Statistics)
  subject:
    topic: Set theory
  classification: QA278.4 .Z47 2012
  related item:
    title info:
      title: Chapman & Hall/CRC machine learning & pattern recognition series
  identifier: 9781439830031 (hardback)
  record info:
    record creation date: 130530
    record change date: 20131204093000.0
    record identifier: 0000083598