Editors:
- Presents machine learning paradigms
- Focuses on recent theory and applications
- Written by experts in the field
Part of the book series: Studies in Computational Intelligence (SCI, volume 801)
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Table of contents (22 chapters)
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Front Matter
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Machine Learning in Classification and Ontology
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Front Matter
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Bio-inspiring Optimization and Applications
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Front Matter
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About this book
The concept of machine learning (ML) is not new in the field of computing. However, due to the ever-changing nature of requirements in today’s world it has emerged in the form of completely new avatars. Now everyone is talking about ML-based solution strategies for a given problem set. The book includes research articles and expository papers on the theory and algorithms of machine learning and bio-inspiring optimization, as well as papers on numerical experiments and real-world applications.
Editors and Affiliations
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Faculty of Computers and Information, Cairo University, Giza, Egypt
Aboul Ella Hassanien
Bibliographic Information
Book Title: Machine Learning Paradigms: Theory and Application
Editors: Aboul Ella Hassanien
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-030-02357-7
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-02356-0Published: 21 December 2018
eBook ISBN: 978-3-030-02357-7Published: 08 December 2018
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: IX, 474
Number of Illustrations: 90 b/w illustrations, 152 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Pattern Recognition