Learning Classifier Systems

Pier L. Lanzi

ISBN 10: 3540677291 / ISBN 13: 9783540677291

Edité par Springer Berlin Heidelberg, Springer Berlin Heidelberg Jun 2000, 2000

A propos de cet article

Neuware -Learning Classifier Systems (LCS) are a machine learning paradigm introduced by John Holland in 1976. They are rule-based systems in which learning is viewed as a process of ongoing adaptation to a partially unknown environment through genetic algorithms and temporal difference learning. This book provides a unique survey of the current state of the art of LCS and highlights some of the most promising research directions. The first part presents various views of leading people on what learning classifier systems are. The second part is devoted to advanced topics of current interest, including alternative representations, methods for evaluating rule utility, and extensions to existing classifier system models. The final part is dedicated to promising applications in areas like data mining, medical data analysis, economic trading agents, aircraft maneuvering, and autonomous robotics. An appendix comprising 467 entries provides a comprehensive LCS bibliography.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 364 pp. Englisch.

N° de réf. du vendeur 9783540677291

Détails bibliographiques

• Titre: Learning Classifier Systems
• Éditeur: Springer Berlin Heidelberg, Springer Berlin Heidelberg Jun 2000
• Date de parution: 2000
• Langue: anglais
• ISBN à 10 chiffres: 3540677291
• ISBN à 13 chiffres: 9783540677291
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 235 L x 155 H x 20 P (en millimètres) 235 millimètres de largeur par 155 millimètres de hauteur par 20 millimètres de profondeur
• Poids: 552 grammes

A propos de ce titre

Synopsis

Learning Classifier Systems (LCS) are a machine learning paradigm introduced by John Holland in 1976. They are rule-based systems in which learning is viewed as a process of ongoing adaptation to a partially unknown environment through genetic algorithms and temporal difference learning. This book provides a unique survey of the current state of the art of LCS and highlights some of the most promising research directions. The first part presents various views of leading people on what learning classifier systems are. The second part is devoted to advanced topics of current interest, including alternative representations, methods for evaluating rule utility, and extensions to existing classifier system models. The final part is dedicated to promising applications in areas like data mining, medical data analysis, economic trading agents, aircraft maneuvering, and autonomous robotics. An appendix comprising 467 entries provides a comprehensive LCS bibliography.