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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, automatically generating programs via metaheuristics, as a form of evolutionary computation and swarm intelligence, has now gained widespread popularity. This book investigates different ways of integrating metaheuristics into machine learning techniques, from both theoretical and practical standpoints. It explores how metaheuristics can be adapted in order to enhance machine learning tools and presents an overview of the main metaheuristic programming methods. Moreover, real-world applications are provided for illustration, e.g., in clustering, big data, machine health monitoring, underwater sonar targets, and banking.
Lingua: Inglese
Editore: Springer Nature Singapore, Springer Nature Singapore Mär 2024, 2024
ISBN 10: 9811938903 ISBN 13: 9789811938900
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware -Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, automatically generating programs via metaheuristics, as a form of evolutionary computation and swarm intelligence, has now gained widespread popularity. This book investigates different ways of integrating metaheuristics into machine learning techniques, from both theoretical and practical standpoints. It explores how metaheuristics can be adapted in order to enhance machine learning tools and presents an overview of the main metaheuristic programming methods. Moreover, real-world applications are provided for illustration, e.g., in clustering, big data, machine health monitoring, underwater sonar targets, and banking.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 240 pp. Englisch.
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Aggiungi al carrelloCondizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer, Berlin|Springer Nature Singapore|Springer, 2024
ISBN 10: 9811938903 ISBN 13: 9789811938900
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, automatically generating p.
Lingua: Inglese
Editore: Springer Nature Singapore, Springer Nature Singapore Mär 2024, 2024
ISBN 10: 9811938903 ISBN 13: 9789811938900
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, automatically generating programs via metaheuristics, as a form of evolutionary computation and swarm intelligence, has now gained widespread popularity. This book investigates different ways of integrating metaheuristics into machine learning techniques, from both theoretical and practical standpoints. It explores how metaheuristics can be adapted in order to enhance machine learning tools and presents an overview of the main metaheuristic programming methods. Moreover, real-world applications are provided for illustration, e.g., in clustering, big data, machine health monitoring, underwater sonar targets, and banking. 240 pp. Englisch.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Metaheuristics for Machine Learning | New Advances and Tools | Mansour Eddaly (u. a.) | Taschenbuch | xv | Englisch | 2024 | Springer | EAN 9789811938900 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.