Handbook of Evolutionary Machine Learning - Couverture souple

9789819938162: Handbook of Evolutionary Machine Learning

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ISBN 10 : 9819938163 ISBN 13 : 9789819938162

Editeur : Springer Verlag, Singapore, 2024

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�diteurSpringer Verlag, Singapore
Date d'�dition2024
ISBN 10 9819938163
ISBN 13 9789819938162
ReliureBroch�
Langueanglais
Nombre de pages768
�diteurBanzhaf Wolfgang, Machado Penousal, Zhang Mengjie

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ISBN 10 : 9819938139 ISBN 13 : 9789819938131
Editeur : Springer Verlag, Singapore, 2023
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Handbook of Evolutionary Machine Learning

Wolfgang Banzhaf

Edit� par Springer Nature Singapore, Springer Nature Singapore Nov 2024, 2024

ISBN 10 : 9819938163 ISBN 13 : 9789819938162

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts. The first part introduces some fundamental concepts and overviews of evolutionary approaches to the three different classes of learning employed in machine learning. The second addresses the use of evolutionary computation as a machine learning technique describing methodologic improvements for evolutionary clustering, classification, regression, and ensemble learning. The third part explores the connection between evolution and neural networks, in particular the connection to deep learning, generative and adversarial models as well as the exciting potential of evolution with large language models. The fourth part focuses on the use of evolutionary computation for supporting machine learning methods. This includes methodological developments for evolutionary data preparation, model parametrization, design, and validation. The final part covers several chapters on applications in medicine, robotics, science, finance, and other disciplines. Readers find reviews of application areas and can discover large-scale, real-world applications of evolutionary machine learning to a variety of problem domains.This book will serve as an essential reference for researchers, postgraduate students, practitioners in industry and all those interested in evolutionary approaches to machine learning. 784 pp. Englisch. N� de r�f. du vendeur 9789819938162

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Handbook of Evolutionary Machine Learning

Wolfgang Banzhaf

Edit� par Springer Nature Singapore, Springer Nature Singapore, 2024

ISBN 10 : 9819938163 ISBN 13 : 9789819938162

Neuf Taschenbuch

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts. The first part introduces some fundamental concepts and overviews of evolutionary approaches to the three different classes of learning employed in machine learning. The second addresses the use of evolutionary computation as a machine learning technique describing methodologic improvements for evolutionary clustering, classification, regression, and ensemble learning. The third part explores the connection between evolution and neural networks, in particular the connection to deep learning, generative and adversarial models as well as the exciting potential of evolution with large language models. The fourth part focuses on the use of evolutionary computation for supporting machine learning methods. This includes methodological developments for evolutionary data preparation, model parametrization, design, and validation. The final part covers several chapters on applications in medicine, robotics, science, finance, and other disciplines. Readers find reviews of application areas and can discover large-scale, real-world applications of evolutionary machine learning to a variety of problem domains.This book will serve as an essential reference for researchers, postgraduate students, practitioners in industry and all those interested in evolutionary approaches to machine learning. N� de r�f. du vendeur 9789819938162

Contacter le vendeur