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: Rule Based Systems for Big Data
    sub title: A Machine Learning Approach
  name:
    name part: Liu, Han.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Gegov, Alexander.
    role:
      role term: author.
  name:
    name part: Cocea, Mihaela.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2016
    edition: 1st ed. 2015.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIII, 121 p. 38 illus., 5 illus. in color. online resource.
  abstract: The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data. The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems.
  table of contents: Introduction -- Theoretical Preliminaries -- Generation of Classification Rules -- Simplification of Classification Rules -- Representation of Classification Rules -- Ensemble Learning Approaches -- Interpretability Analysis.
  note: by Han Liu, Alexander Gegov, Mihaela Cocea.
  subject:
    topic: Engineering
  subject:
    topic: Data mining
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computational intelligence
  subject:
    topic: Engineering
  subject:
    topic: Computational Intelligence
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Data Mining and Knowledge Discovery
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Big Data, 13
  identifier: 9783319236964
  identifier: http://dx.doi.org/10.1007/978-3-319-23696-4
  record info:
    record creation date: 150909
    record change date: 20151204190928.0
    record identifier: 978-3-319-23696-4