Foundations of Learning Classifier Systems
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This volume brings together recent theoretical work in Learning Classifier Systems (LCS), which is a Machine Learning technique combining Genetic Algorithms and Reinforcement Learning. It includes self-contained background chapters on related fields (reinforcement learning and evolutionary computation) tailored for a classifier systems audience and written by acknowledged authorities in their area - as well as a relevant historical original work by John Holland. …mehr

Produktbeschreibung
This volume brings together recent theoretical work in Learning Classifier Systems (LCS), which is a Machine Learning technique combining Genetic Algorithms and Reinforcement Learning. It includes self-contained background chapters on related fields (reinforcement learning and evolutionary computation) tailored for a classifier systems audience and written by acknowledged authorities in their area - as well as a relevant historical original work by John Holland.
  • Produktdetails
  • Studies in Fuzziness and Soft Computing 183
  • Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
  • Softcover reprint of hardcover 1st ed. 2005
  • Seitenzahl: 344
  • Erscheinungstermin: 25. November 2010
  • Englisch
  • Abmessung: 235mm x 155mm x 18mm
  • Gewicht: 521g
  • ISBN-13: 9783642064135
  • ISBN-10: 3642064132
  • Artikelnr.: 32028145
Inhaltsangabe
Section 1 - Rule Discovery. Population Dynamics of Genetic Algorithms. Approximating Value Functions in Classifier Systems. Two Simple Learning Classifier Systems. Computational Complexity of the XCS Classifier System. An Analysis of Continuous-Valued Representations for Learning Classifier Systems.- Section 2 - Credit Assignment. Reinforcement Learning: a Brief Overview. A Mathematical Framework for Studying Learning Classifier Systems. Rule Fitness and Pathology in Learning Classifier Systems. Learning Classifier Systems: A Reinforcement Learning Perspective. Learning Classifier Systems with Convergence and Generalization.- Section 3 - Problem Characterization. On the Classification of Maze Problems. What Makes a Problem Hard?