Metaheuristics in Machine Learning: Theory and Applications

Lieferzeit: Lieferbar innerhalb 14 Tagen

171,19 

Studies in Computational Intelligence 967

ISBN: 3030705447
ISBN 13: 9783030705442
Herausgeber: Diego Oliva/Essam H Houssein/Salvador Hinojosa
Verlag: Springer Verlag GmbH
Umfang: xiv, 769 S., 77 s/w Illustr., 226 farbige Illustr., 769 p. 303 illus., 226 illus. in color.
Erscheinungsdatum: 15.07.2022
Auflage: 1/2022
Produktform: Kartoniert
Einband: Kartoniert

This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.

Artikelnummer: 6118438 Kategorie:

Beschreibung

This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities. 

Herstellerkennzeichnung:


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E-Mail: juergen.hartmann@springer.com

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