Foundations of learning classifier systems

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 computati...

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Opis bibliograficzny
1. autor: Bull, Larry
Kolejni autorzy: Kovacs, Tim (Redaktor), Bull, Larry, 19..- (Dyrektor wydawnictwa), Kovacs, Tim, 19..- (Dyrektor wydawnictwa)
Format: Livre numérique
Język:Anglais
Wydane: Berlin, Heidelberg : Springer Berlin Heidelberg [20..].
Cham : Springer Nature
Wydanie:1st ed. 2005.
Seria:Studies in Fuzziness and Soft Computing 183
Dostęp online:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Accès Université d'Orléans
Accès INSA CVL
Komentarz: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Foundations of learning classifier systems, Larry Bull, Tim Kovacs, Berlin, Springer-Verlag, 2005, 1 vol. (VI-336 p.), Studies in fuzziness and soft computing, 3-540-25073-5
• Foundations of Learning Classifier Systems, Texte imprimé, 9783540808282
• Foundations of Learning Classifier Systems, Texte imprimé, 9783642064135
• Foundations of learning classifier systems, Larry Bull, Tim Kovacs, Berlin, Springer-Verlag, 2005, 1 vol. (VI-336 p.), Studies in fuzziness and soft computing, 3-540-25073-5
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505 1 |a 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? 
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520 |a 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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