Strength or Accuracy
Kovacs, Tim
ISBN 10: 1447110587 ISBN 13: 9781447110583
Editore: Springer, 2012
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Descrizione:
Codice articolo 19199635-n
Riassunto:
A detailed examination of learning classifier systems (LCS), a form of machine learning system, which incorporates both Evolutionary Algorithms and Reinforcement Learning Algorithms.
Recensione:
From the reviews:
"This book is a monograph on learning classifier systems ... . The main objective of the book is to compare strength-based classifier systems with accuracy-based systems. ... The book is equipped with nine appendices. ... The biggest advantage of the book is its readability. The book is well written and is illustrated with many convincing examples." (Jerzy W. Grzymal-Busse, Mathematical Reviews, Issue 2005 k)
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Titolo: Strength or Accuracy
Casa editrice: Springer
Data di pubblicazione: 2012
Legatura: Brossura
Condizione: New
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Strength or Accuracy: Credit Assignment in Learning Classifier Systems
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. There are few texts that deal with learning classifier systems at all most include only a chapter or two on them, and are out of dateThe study of learning classifier systems has made great progress in the last few years, and is an increasingly ac. Codice articolo 4183943
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Strength or Accuracy: Credit Assignment in Learning Classifier Systems
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Taschenbuch. Condizione: Neu. Strength or Accuracy: Credit Assignment in Learning Classifier Systems | Tim Kovacs | Taschenbuch | xvi | Englisch | 2012 | Springer London | EAN 9781447110583 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. Codice articolo 106119061
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Strength or Accuracy: Credit Assignment in Learning Classifier Systems
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Springer London Okt 2012, 2012
ISBN 10: 1447110587
ISBN 13: 9781447110583
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Classifier systems are an intriguing approach to a broad range of machine learning problems, based on automated generation and evaluation of condi tion/action rules. Inreinforcement learning tasks they simultaneously address the two major problems of learning a policy and generalising over it (and re lated objects, such as value functions). Despite over 20 years of research, however, classifier systems have met with mixed success, for reasons which were often unclear. Finally, in 1995 Stewart Wilson claimed a long-awaited breakthrough with his XCS system, which differs from earlier classifier sys tems in a number of respects, the most significant of which is the way in which it calculates the value of rules for use by the rule generation system. Specifically, XCS (like most classifiersystems) employs a genetic algorithm for rule generation, and the way in whichit calculates rule fitness differsfrom earlier systems. Wilson described XCS as an accuracy-based classifiersystem and earlier systems as strength-based. The two differin that in strength-based systems the fitness of a rule is proportional to the return (reward/payoff) it receives, whereas in XCS it is a function of the accuracy with which return is predicted. The difference is thus one of credit assignment, that is, of how a rule's contribution to the system's performance is estimated. XCS is a Q learning system; in fact, it is a proper generalisation of tabular Q-learning, in which rules aggregate states and actions. In XCS, as in other Q-learners, Q-valuesare used to weightaction selection. 328 pp. Englisch. Codice articolo 9781447110583
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Strength or Accuracy: Credit Assignment in Learning Classifier Systems
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Springer London, Springer London Okt 2012, 2012
ISBN 10: 1447110587
ISBN 13: 9781447110583
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Classifier systems are an intriguing approach to a broad range of machine learning problems, based on automated generation and evaluation of condi tion/action rules. Inreinforcement learning tasks they simultaneously address the two major problems of learning a policy and generalising over it (and re lated objects, such as value functions). Despite over 20 years of research, however, classifier systems have met with mixed success, for reasons which were often unclear. Finally, in 1995 Stewart Wilson claimed a long-awaited breakthrough with his XCS system, which differs from earlier classifier sys tems in a number of respects, the most significant of which is the way in which it calculates the value of rules for use by the rule generation system. Specifically, XCS (like most classifiersystems) employs a genetic algorithm for rule generation, and the way in whichit calculates rule fitness differsfrom earlier systems. Wilson described XCS as an accuracy-based classifiersystem and earlier systems as strength-based. The two differin that in strength-based systems the fitness of a rule is proportional to the return (reward/payoff) it receives, whereas in XCS it is a function of the accuracy with which return is predicted. The difference is thus one of credit assignment, that is, of how a rule's contribution to the system's performance is estimated. XCS is a Q learning system; in fact, it is a proper generalisation of tabular Q-learning, in which rules aggregate states and actions. In XCS, as in other Q-learners, Q-valuesare used to weightaction selection.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 328 pp. Englisch. Codice articolo 9781447110583
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Strength or Accuracy: Credit Assignment in Learning Classifier Systems
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Springer London, Springer London, 2012
ISBN 10: 1447110587
ISBN 13: 9781447110583
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Classifier systems are an intriguing approach to a broad range of machine learning problems, based on automated generation and evaluation of condi tion/action rules. Inreinforcement learning tasks they simultaneously address the two major problems of learning a policy and generalising over it (and re lated objects, such as value functions). Despite over 20 years of research, however, classifier systems have met with mixed success, for reasons which were often unclear. Finally, in 1995 Stewart Wilson claimed a long-awaited breakthrough with his XCS system, which differs from earlier classifier sys tems in a number of respects, the most significant of which is the way in which it calculates the value of rules for use by the rule generation system. Specifically, XCS (like most classifiersystems) employs a genetic algorithm for rule generation, and the way in whichit calculates rule fitness differsfrom earlier systems. Wilson described XCS as an accuracy-based classifiersystem and earlier systems as strength-based. The two differin that in strength-based systems the fitness of a rule is proportional to the return (reward/payoff) it receives, whereas in XCS it is a function of the accuracy with which return is predicted. The difference is thus one of credit assignment, that is, of how a rule's contribution to the system's performance is estimated. XCS is a Q learning system; in fact, it is a proper generalisation of tabular Q-learning, in which rules aggregate states and actions. In XCS, as in other Q-learners, Q-valuesare used to weightaction selection. Codice articolo 9781447110583
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Strength or Accuracy
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