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 Evolutionary Online Learning Systems
    sub title: A Principled Approach to LCS Analysis and Design
  name:
    name part: Butz, Martin V.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2006
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXI, 259 p. online resource.
  abstract: This book offers a comprehensive introduction to learning classifier systems (LCS) – or more generally, rule-based evolutionary online learning systems. LCSs learn interactively – much like a neural network – but with an increased adaptivity and flexibility. This book provides the necessary background knowledge on problem types, genetic algorithms, and reinforcement learning as well as a principled, modular analysis approach to understand, analyze, and design LCSs. The analysis is exemplarily carried through on the XCS classifier system – the currently most prominent system in LCS research. Several enhancements are introduced to XCS and evaluated. An application suite is provided including classification, reinforcement learning and data-mining problems. Reconsidering John Holland’s original vision, the book finally discusses the current potentials of LCSs for successful applications in cognitive science and related areas.
  table of contents: Prerequisites -- Simple Learning Classifier Systems -- The XCS Classifier System -- How XCS Works: Ensuring Effective Evolutionary Pressures -- When XCS Works: Towards Computational Complexity -- Effective XCS Search: Building Block Processing -- XCS in Binary Classification Problems -- XCS in Multi-Valued Problems -- XCS in Reinforcement Learning Problems -- Facetwise LCS Design -- Towards Cognitive Learning Classifier Systems -- Summary and Conclusions.
  note: by Martin V. Butz.
  subject:
    topic: Computer science
  subject:
    topic: Neurosciences
  subject:
    topic: Computers
  subject:
    topic: Artificial intelligence
  subject:
    topic: Applied mathematics
  subject:
    topic: Engineering mathematics
  subject:
    topic: Computer Science
  subject:
    topic: Theory of Computation
  subject:
    topic: Appl.Mathematics/Computational Methods of Engineering
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Neurosciences
  subject:
    topic: Applications of Mathematics
  classification: QA75.5-76.95
  classification: 004.0151
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Fuzziness and Soft Computing, 191
  identifier: 9783540312314
  identifier: http://dx.doi.org/10.1007/b104669
  record info:
    record creation date: 100301
    record change date: 20151204170815.0
    record identifier: 978-3-540-31231-4