Description: Metaheuristics in Machine Learning: Theory and Applications Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). Author(s): Diego Oliva, Essam H. Houssein, Salvador Hinojosa Format: Hardback Publisher: Springer Nature Switzerland AG, Switzerland Imprint: Springer Nature Switzerland AG ISBN-13: 9783030705411, 978-3030705411 Synopsis 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.
Price: 112.88 GBP
Location: Aldershot
End Time: 2025-01-12T09:22:32.000Z
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Book Title: Metaheuristics in Machine Learning: Theory and Applications
Item Height: 235 mm
Item Width: 155 mm
Series: Studies in Computational Intelligence
Author: Essam H. Houssein, Salvador Hinojosa, Diego Oliva
Publication Name: Metaheuristics in Machine Learning: Theory and Applications
Format: Hardcover
Language: English
Publisher: Springer Nature Switzerland A&G
Subject: Computer Science
Publication Year: 2021
Type: Textbook
Item Weight: 1340 g
Number of Pages: 769 Pages
