Foundations of Learning Classifier Systems
(Sprache: Englisch)
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...
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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.
Inhaltsverzeichnis zu „Foundations of Learning Classifier Systems “
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 SystemsSection 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?
Bibliographische Angaben
- 2005, 336 Seiten, 98 Schwarz-Weiss-Abbildungen, Masse: 16,2 x 24,3 cm, Gebunden, Englisch
- Herausgegeben: Larry Bull, Tim Kovacs
- Verlag: Springer
- ISBN-10: 3540250735
- ISBN-13: 9783540250739
- Erscheinungsdatum: 22.07.2005
Sprache:
Englisch
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