Metaheuristics in Machine Learning: Theory and Applications / Studies in Computational Intelligence Bd.967 (PDF)
(Sprache: Englisch)
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...
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Produktinformationen zu „Metaheuristics in Machine Learning: Theory and Applications / Studies in Computational Intelligence Bd.967 (PDF)“
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.
Bibliographische Angaben
- 2021, 1st ed. 2021, 769 Seiten, Englisch
- Herausgegeben: Diego Oliva, Essam H. Houssein, Salvador Hinojosa
- Verlag: Springer International Publishing
- ISBN-10: 3030705420
- ISBN-13: 9783030705428
- Erscheinungsdatum: 13.07.2021
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: PDF
- Größe: 19 MB
- Ohne Kopierschutz
- Vorlesefunktion
Sprache:
Englisch
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