Rule-based evolutionary online learning systems : a principled approach to LCS analysis and design

This book offers a comprehensive introduction to learning classifier systems (LCS) or more generally, rule-based evolutionary online learning systems. LCSs learn interactively much like a neural network but with an increased adaptivity and flexibility. This book provides the necessary background kno...

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Gorde:
Xehetasun bibliografikoak
Egile nagusia: Butz, Martin V., 1975-
Formatua: Livre numérique
Hizkuntza:Anglais
Argitaratua: Berlin, Heidelberg : Springer Berlin Heidelberg 2006.
Cham : Springer Nature
Saila:Studies in Fuzziness and Soft Computing 191
Gaiak:
Sarrera elektronikoa:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Accès Université d'Orléans
Accès INSA CVL
Oharra: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
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Edition sous un autre format:• Rule-Based Evolutionary Online Learning Systems, Texte imprimé, 9783642064777
• Rule-Based Evolutionary Online Learning Systems, Texte imprimé, 9783540809371
• Rule-Based Evolutionary Online Learning Systems, Texte imprimé, 9783540253792
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100 1 |a Butz, Martin V.,  |d 1975- 
245 1 0 |a Rule-based evolutionary online learning systems :  |b a principled approach to LCS analysis and design   |c Martin V. Butz ; [foreword by Stewart W. Wilson] ; [foreword by David E. Goldberg]. 
260 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg. 
260 |a Cham :  |b Springer Nature,  |c 2006. 
490 0 |a Studies in Fuzziness and Soft Computing  |v 191  |x 1860-0808 
500 |a Archives Springer e-books (Licence nationale) 
500 |a Archives Springer e-books (Licence nationale) 
505 0 |a Prerequisites -- Simple Learning Classifier Systems -- The XCS Classifier System -- How XCS Works: Ensuring Effective Evolutionary Pressures -- When XCS Works: Towards Computational Complexity -- Effective XCS Search: Building Block Processing -- XCS in Binary Classification Problems -- XCS in Multi-Valued Problems -- XCS in Reinforcement Learning Problems -- Facetwise LCS Design -- Towards Cognitive Learning Classifier Systems -- Summary and Conclusions. 
506 |a Accès en ligne pour les établissements français bénéficiaires des licences nationales 
506 |a Accès soumis à abonnement pour tout autre établissement 
506 |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. chttps://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 
520 |a This book offers a comprehensive introduction to learning classifier systems (LCS) or more generally, rule-based evolutionary online learning systems. LCSs learn interactively much like a neural network but with an increased adaptivity and flexibility. This book provides the necessary background knowledge on problem types, genetic algorithms, and reinforcement learning as well as a principled, modular analysis approach to understand, analyze, and design LCSs. The analysis is exemplarily carried through on the XCS classifier system the currently most prominent system in LCS research. Several enhancements are introduced to XCS and evaluated. An application suite is provided including classification, reinforcement learning and data-mining problems. Reconsidering John Holland s original vision, the book finally discusses the current potentials of LCSs for successful applications in cognitive science and related areas. 
650 |a Systèmes auto-organisés 
776 0 |t Rule-Based Evolutionary Online Learning Systems  |b Texte imprimé  |z 9783642064777 
776 0 |t Rule-Based Evolutionary Online Learning Systems  |b Texte imprimé  |z 9783540809371 
776 0 |t Rule-Based Evolutionary Online Learning Systems  |b Texte imprimé  |z 9783540253792 
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856 4 |5 452349901:747907234  |u https://ezproxy.univ-orleans.fr/login?url=https://doi.org/10.1007/b104669  |z Accès Université d'Orléans 
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