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Foundations of Learning Classifier Systems de Larry Bull

Foundations of Learning Classifier Systems: Studies in Fuzziness and Soft Computing, cartea 183

Editat de Larry Bull, Tim Kovacs
en Limba Engleză Paperback – 25 noi 2010
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.
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Specificații

ISBN-13: 9783642064135
ISBN-10: 3642064132
Pagini: 344
Ilustrații: VI, 336 p.
Dimensiuni: 155 x 235 x 19 mm
Greutate: 0.52 kg
Ediția:Softcover reprint of hardcover 1st edition 2005
Editura: Springer
Colecția Studies in Fuzziness and Soft Computing
Seria Studies in Fuzziness and Soft Computing

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

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?

Textul de pe ultima copertă

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.

Caracteristici

Recent theoretical work in Learning Classifier Systems (LCS) Presents a coherent framework of LCS Includes a relevant historical original work by John Holland Includes supplementary material: sn.pub/extras