Metaheuristics in Machine Learning: Theory and Applications: 967 - Brossura

Brossura

ISBN 10: 3030705447 ISBN 13: 9783030705442

Casa editrice: Springer Nature, 2022

Riassunto

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. 

Su questo libro

Dalla quarta di copertina

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.

Dettagli dell?edizione

• Editore: Springer Nature
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 3030705447
• ISBN 13: 9783030705442
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 788
• Redattore: Oliva Diego, Houssein Essam H., Hinojosa Salvador
• Contatto del produttore: non disponibile