Metaheuristics in Machine Learning: Theory and Applications (1st ed. 2021) (Studies in Computational Intelligence #967)
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- 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.
- Copyright:
- 2021
Book Details
- Book Quality:
- Publisher Quality
- ISBN-13:
- 9783030705428
- Related ISBNs:
- 9783030705411
- Publisher:
- Springer International Publishing
- Date of Addition:
- 10/10/21
- Copyrighted By:
- The Editor
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Computers and Internet, Technology
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.
- Edited by:
- Salvador Hinojosa
- Edited by:
- Essam H. Houssein
- Edited by:
- Diego Oliva
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- by Diego Oliva
- by Salvador Hinojosa
- by Essam H. Houssein
- in Nonfiction
- in Computers and Internet
- in Technology