Metaheuristics in Machine Learning: Theory and Applications

Author:   Diego Oliva ,  Essam H. Houssein ,  Salvador Hinojosa
Publisher:   Springer Nature Switzerland AG
Edition:   2021 ed.
Volume:   967
ISBN:  

9783030705442


Pages:   769
Publication Date:   15 July 2022
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Metaheuristics in Machine Learning: Theory and Applications


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Overview

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. 

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Author:   Diego Oliva ,  Essam H. Houssein ,  Salvador Hinojosa
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   2021 ed.
Volume:   967
Weight:   1.187kg
ISBN:  

9783030705442


ISBN 10:   3030705447
Pages:   769
Publication Date:   15 July 2022
Audience:   Professional and scholarly ,  College/higher education ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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