applications learning classifier systems (34 resultados)

Paperback. Condición: Very Good. Learning Classifier Systems: From Foundations To Applications (Lecture Notes In Computer Science / Lecture Notes In Artificial Intelligence) This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping.

Paperback. Condición: Very Good. This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping.

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Paperback or Softback. Condición: New. Learning Classifier Systems: From Foundations to Applications. Book.

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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Learning Classifier Systems (LCS) are amachine 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.

Taschenbuch. Condición: Neu. Learning Classifier Systems | From Foundations to Applications | Pier L. Lanzi (u. a.) | Taschenbuch | x | Englisch | 2000 | Springer | EAN 9783540677291 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Condición: New. In.

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Condición: New. pp. 316.

Condición: New. pp. 324.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - The field called Learning Classifier Systems is populated with romantics. Why shouldn't it be possible for computer programs to adapt, learn, and develop while interacting with their environments In particular, why not systems that, like organic populations, contain competing, perhaps cooperating, entities evolving together John Holland was one of the earliest scientists with this vision, at a time when so-called artificial intelligence was in its infancy and mainly concerned with preprogrammed systems that didn't learn. that, like organisms, had sensors, took Instead, Holland envisaged systems actions, and had rich self-generated internal structure and processing. In so doing he foresaw and his work prefigured such present day domains as reinforcement learning and embedded agents that are now displacing the older 'standard Af' . One focus was what Holland called 'classifier systems': sets of competing rule like 'classifiers', each a hypothesis as to how best to react to some aspect of the environment--or to another rule. The system embracing such a rule 'popu lation' would explore its available actions and responses, rewarding and rating the active rules accordingly. Then 'good' classifiers would be selected and re produced, mutated and even crossed, a la Darwin and genetics, steadily and reliably increasing the system's ability to cope.

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - The field called Learning Classifier Systems is populated with romantics. Why shouldn't it be possible for computer programs to adapt, learn, and develop while interacting with their environments In particular, why not systems that, like organic populations, contain competing, perhaps cooperating, entities evolving together John Holland was one of the earliest scientists with this vision, at a time when so-called artificial intelligence was in its infancy and mainly concerned with preprogrammed systems that didn't learn. that, like organisms, had sensors, took Instead, Holland envisaged systems actions, and had rich self-generated internal structure and processing. In so doing he foresaw and his work prefigured such present day domains as reinforcement learning and embedded agents that are now displacing the older 'standard Af' . One focus was what Holland called 'classifier systems': sets of competing rule like 'classifiers', each a hypothesis as to how best to react to some aspect of the environment--or to another rule. The system embracing such a rule 'popu lation' would explore its available actions and responses, rewarding and rating the active rules accordingly. Then 'good' classifiers would be selected and re produced, mutated and even crossed, a la Darwin and genetics, steadily and reliably increasing the system's ability to cope.

Hardcover. Condición: Like New. Like New. book.

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Condición: new. Questo è un articolo print on demand.

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The field called Learning Classifier Systems is populated with romantics. Why shouldn't it be possible for computer programs to adapt, learn, and develop while interacting with their environments In particular, why not systems that, like organic populations, contain competing, perhaps cooperating, entities evolving together John Holland was one of the earliest scientists with this vision, at a time when so-called artificial intelligence was in its infancy and mainly concerned with preprogrammed systems that didn't learn. that, like organisms, had sensors, took Instead, Holland envisaged systems actions, and had rich self-generated internal structure and processing. In so doing he foresaw and his work prefigured such present day domains as reinforcement learning and embedded agents that are now displacing the older 'standard Af' . One focus was what Holland called 'classifier systems': sets of competing rule like 'classifiers', each a hypothesis as to how best to react to some aspect of the environment--or to another rule. The system embracing such a rule 'popu lation' would explore its available actions and responses, rewarding and rating the active rules accordingly. Then 'good' classifiers would be selected and re produced, mutated and even crossed, a la Darwin and genetics, steadily and reliably increasing the system's ability to cope. 320 pp. Englisch.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The field called Learning Classifier Systems is populated with romantics. Why shouldn't it be possible for computer programs to adapt, learn, and develop while interacting with their environments In particular, why not systems that, like organic populations, contain competing, perhaps cooperating, entities evolving together John Holland was one of the earliest scientists with this vision, at a time when so-called artificial intelligence was in its infancy and mainly concerned with preprogrammed systems that didn't learn. that, like organisms, had sensors, took Instead, Holland envisaged systems actions, and had rich self-generated internal structure and processing. In so doing he foresaw and his work prefigured such present day domains as reinforcement learning and embedded agents that are now displacing the older 'standard Af' . One focus was what Holland called 'classifier systems': sets of competing rule like 'classifiers', each a hypothesis as to how best to react to some aspect of the environment--or to another rule. The system embracing such a rule 'popu lation' would explore its available actions and responses, rewarding and rating the active rules accordingly. Then 'good' classifiers would be selected and re produced, mutated and even crossed, a la Darwin and genetics, steadily and reliably increasing the system's ability to cope. 316 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Brings together recent real-world applications of a machine learning technique whose performance has been greatly improved in recent years and which is experiencing resurgence in interestThe field called Learning Classifier Systems is populated w.

Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Brings together recent real-world applications of a machine learning technique whose performance has been greatly improved in recent years and which is experiencing resurgence in interestThe field called Learning Classifier Systems is populated w.

Buch. Condición: Neu. Applications of Learning Classifier Systems | Larry Bull | Buch | Studies in Fuzziness and Soft Computing | Einband - fest (Hardcover) | Englisch | 2004 | Springer | EAN 9783540211099 | Verantwortliche Person für die EU: Springer Heidelberg, Tiergartenstr. 17, 69121 Heidelberg, buchhandel-buch[at]springer[dot]com | Anbieter: preigu Print on Demand.

Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The field called Learning Classifier Systems is populated with romantics. Why shouldn't it be possible for computer programs to adapt, learn, and develop while interacting with their environments In particular, why not systems that, like organic populations, contain competing, perhaps cooperating, entities evolving together John Holland was one of the earliest scientists with this vision, at a time when so-called artificial intelligence was in its infancy and mainly concerned with preprogrammed systems that didn't learn. that, like organisms, had sensors, took Instead, Holland envisaged systems actions, and had rich self-generated internal structure and processing. In so doing he foresaw and his work prefigured such present day domains as reinforcement learning and embedded agents that are now displacing the older 'standard Af' . One focus was what Holland called 'classifier systems': sets of competing rule like 'classifiers', each a hypothesis as to how best to react to some aspect of the environment--or to another rule. The system embracing such a rule 'popu lation' would explore its available actions and responses, rewarding and rating the active rules accordingly. Then 'good' classifiers would be selected and re produced, mutated and even crossed, a la Darwin and genetics, steadily and reliably increasing the system's ability to cope.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 320 pp. Englisch.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The field called Learning Classifier Systems is populated with romantics. Why shouldn't it be possible for computer programs to adapt, learn, and develop while interacting with their environments In particular, why not systems that, like organic populations, contain competing, perhaps cooperating, entities evolving together John Holland was one of the earliest scientists with this vision, at a time when so-called artificial intelligence was in its infancy and mainly concerned with preprogrammed systems that didn't learn. that, like organisms, had sensors, took Instead, Holland envisaged systems actions, and had rich self-generated internal structure and processing. In so doing he foresaw and his work prefigured such present day domains as reinforcement learning and embedded agents that are now displacing the older 'standard Af' . One focus was what Holland called 'classifier systems': sets of competing rule like 'classifiers', each a hypothesis as to how best to react to some aspect of the environment--or to another rule. The system embracing such a rule 'popu lation' would explore its available actions and responses, rewarding and rating the active rules accordingly. Then 'good' classifiers would be selected and re produced, mutated and even crossed, a la Darwin and genetics, steadily and reliably increasing the system's ability to cope.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 316 pp. Englisch.