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: Nonparametric Kernel Density Estimation and Its Computational Aspects
  name:
    name part: Gramacki, Artur.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXIX, 176 p. 70 illus. online resource.
  abstract: This book describes computational problems related to kernel density estimation (KDE) - one of the most important and widely used data smoothing techniques. A very detailed description of novel FFT-based algorithms for both KDE computations and bandwidth selection are presented. The theory of KDE appears to have matured and is now well developed and understood. However, there is not much progress observed in terms of performance improvements. This book is an attempt to remedy this. The book primarily addresses researchers and advanced graduate or postgraduate students who are interested in KDE and its computational aspects. The book contains both some background and much more sophisticated material, hence also more experienced researchers in the KDE area may find it interesting. The presented material is richly illustrated with many numerical examples using both artificial and real datasets. Also, a number of practical applications related to KDE are presented.
  note: by Artur Gramacki.
  subject:
    topic: Computational intelligence
  subject:
    topic: Artificial intelligence
  subject:
    topic: Big data
  subject:
    topic: Computational Intelligence
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Big Data
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Big Data, 37
  identifier: 9783319716886
  identifier: https://doi.org/10.1007/978-3-319-71688-6
  record info:
    record creation date: 171221
    record change date: 20191021211408.0
    record identifier: 978-3-319-71688-6