Metaheuristics for Machine Learning: New Advances and Tools - Rilegato

 
9789811938870: Metaheuristics for Machine Learning: New Advances and Tools
Rilegato
ISBN 10: 9811938873 ISBN 13: 9789811938870
Casa editrice: Springer-Nature New York Inc, 2023

Sinossi

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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sull?autore

Mansour Eddaly is an assistant professor in the College of Business and Economics at Qassim University (KSA). His current research interests mainly involve combinatorial optimization, metaheuristics, and computational intelligence.

Bassem Jarboui is Full Professor of Operational Research at Sfax University, Tunisia, where he also completed his PhD. Currently, he is working at the Higher Colleges of Technology, Abu Dhabi, UAE. He has edited seven books and two special journal issues. He has also organized and chaired five international conferences. He has published over 130 scientific papers, including articles, contributions to edited proceedings, and book chapters.

Patrick Siarry received his PhD from the University of Paris 6 in 1986 and his Doctor of Sciences (Habilitation) from the University of Paris 11 in 1994. He first became involved in the development of analogue and digital models of nuclear power plants at Électricité de France (E.D.F.). He has been Professor of Automatics and Informatics since 1995. His main research interest is in the applications of new stochastic global optimization heuristics to various engineering fields.


Dalla quarta di copertina

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.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer-Nature New York Inc
Data di pubblicazione
2023
Lingua
Inglese
ISBN 10 
9811938873
ISBN 13 
9789811938870
Rilegatura
Copertina rigida
Numero di pagine
240
Redattore
Eddaly Mansour, Jarboui Bassem, Siarry Patrick
Contatto del produttore
ProductSafety@springernature.com
ProductSafety@springernature.com

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Darmstadt
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Altre edizioni note dello stesso titolo

9789811938894: Metaheuristics for Machine Learning: New Advances and Tools

Edizione in evidenza

ISBN 10:  981193889X ISBN 13:  9789811938894
Casa editrice: Springer, 2023
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