Foundations of Learning Classifier Systems
Paperback
Lieferzeit: Print on Demand - Lieferbar innerhalb von 3-5 Werktagen I
ISBN-13:
9783642064135
Veröffentl:
2010
Einband:
Paperback
Erscheinungsdatum:
25.11.2010
Seiten:
344
Autor:
Tim Kovacs
Gewicht:
522 g
Format:
235x155x19 mm
Serie:
183, Studies in Fuzziness and Soft Computing
Sprache:
Englisch
Beschreibung:
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.
This volume brings together recent theoretical work in Learning Classifier Systems (LCS), which is a Machine Learning technique combining Genetic Algorithms and Reinforcement Learning.
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?