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: Learning Classifier Systems
    sub title: From Foundations to Applications
  name:
    name part: Lanzi, Pier L.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Stolzmann, Wolfgang.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Wilson, Stewart W.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2000
    edition: 1st ed. 2000.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: X, 354 p. online resource.
  abstract: Learning Classifier Systems (LCS) are a machine learning paradigm introduced by John Holland in 1976. They are rule-based systems in which learning is viewed as a process of ongoing adaptation to a partially unknown environment through genetic algorithms and temporal difference learning. This book provides a unique survey of the current state of the art of LCS and highlights some of the most promising research directions. The first part presents various views of leading people on what learning classifier systems are. The second part is devoted to advanced topics of current interest, including alternative representations, methods for evaluating rule utility, and extensions to existing classifier system models. The final part is dedicated to promising applications in areas like data mining, medical data analysis, economic trading agents, aircraft maneuvering, and autonomous robotics. An appendix comprising 467 entries provides a comprehensive LCS bibliography.
  table of contents: Basics -- What Is a Learning Classifier System? -- A Roadmap to the Last Decade of Learning Classifier System Research (From 1989 to 1999) -- State of XCS Classifier System Research -- An Introduction to Learning Fuzzy Classifier Systems -- Advanced Topics -- Fuzzy and Crisp Representations of Real-Valued Input for Learning Classifier Systems -- Do We Really Need to Estimate Rule Utilities in Classifier Systems? -- Strength or Accuracy? Fitness Calculation in Learning Classifier Systems -- Non-homogeneous Classifier Systems in a Macro-evolution Process -- An Introduction to Anticipatory Classifier Systems -- A Corporate XCS -- Get Real! XCS with Continuous-Valued Inputs -- Applications -- XCS and the Monk's Problems -- Learning Classifier Systems Applied to Knowledge Discovery in Clinical Research Databases -- An Adaptive Agent Based Economic Model -- The Fighter Aircraft LCS: A Case of Different LCS Goals and Techniques -- Latent Learning and Action Planning in Robots with Anticipatory Classifier Systems -- The Bibliography -- A Learning Classifier Systems Bibliography.
  note: edited by Pier L. Lanzi, Wolfgang Stolzmann, Stewart W. Wilson.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Mathematical logic
  subject:
    topic: Computers
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Mathematical Logic and Formal Languages
  subject:
    topic: Computation by Abstract Devices
  classification: Q334-342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Lecture Notes in Artificial Intelligence ; 1813
  identifier: 9783540450276
  identifier: https://doi.org/10.1007/3-540-45027-0
  record info:
    record creation date: 121227
    record change date: 20191024191615.0
    record identifier: 978-3-540-45027-6