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: Synergies of Soft Computing and Statistics for Intelligent Data Analysis
  name:
    name part: Kruse, Rudolf.
    role:
      role term: editor.
  name:
    name part: Berthold, Michael R.
    role:
      role term: editor.
  name:
    name part: Moewes, Christian.
    role:
      role term: editor.
  name:
    name part: Gil, María Ángeles.
    role:
      role term: editor.
  name:
    name part: Grzegorzewski, Przemysław.
    role:
      role term: editor.
  name:
    name part: Hryniewicz, Olgierd.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2013
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 584 p. 74 illus. online resource.
  abstract: In recent years there has been a growing interest to extend classical methods for data analysis. The aim is to allow a more flexible modeling of phenomena such as uncertainty, imprecision or ignorance. Such extensions of classical probability theory and statistics are useful in many real-life situations, since uncertainties in data are not only present in the form of randomness --- various types of incomplete or subjective information have to be handled. About twelve years ago the idea of strengthening the dialogue between the various research communities in the field of data analysis was born and resulted in the International Conference Series on Soft Methods in Probability and Statistics (SMPS). This book gathers contributions presented at the SMPS'2012 held in Konstanz, Germany. Its aim is to present recent results illustrating new trends in intelligent data analysis. It gives a comprehensive overview of current research into the fusion of soft computing methods with probability and statistics. Synergies of both fields might improve intelligent data analysis methods in terms of robustness to noise and applicability to larger datasets, while being able to efficiently obtain understandable solutions of real-world problems.
  table of contents: PART I Invited Papers -- PART II Foundations -- PART III Statistical Methods -- PART IV Mathematical Aspects -- PART V Engineering.
  note: edited by Rudolf Kruse, Michael R. Berthold, Christian Moewes, María Ángeles Gil, Przemysław Grzegorzewski, Olgierd Hryniewicz.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computational intelligence
  subject:
    topic: Engineering
  subject:
    topic: Computational Intelligence
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Advances in Intelligent Systems and Computing, 190
  identifier: 9783642330421
  identifier: http://dx.doi.org/10.1007/978-3-642-33042-1
  record info:
    record creation date: 120913
    record change date: 20151030101812.0
    record identifier: 978-3-642-33042-1