Foundations of Learning Classifier Systems

Herausgegeben:Bull, Larry; Kovacs, Tim

• Broschiertes Buch

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
• Artikelnr. des Verlages: 978-3-642-06413-5
• Softcover reprint of hardcover 1st ed. 2005
• Seitenzahl: 344
• Erscheinungstermin: 25. November 2010
• Englisch
• Abmessung: 235mm x 155mm x 19mm
• 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?