Overview
- Presents applications of learning and analytics methodologies in intelligent systems and various technological fields
- Highlights the latest research on machine learning paradigms
- Written by recognized experts in the field
Part of the book series: Learning and Analytics in Intelligent Systems (LAIS, volume 1)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (16 chapters)
-
Learning and Analytics in Intelligent Imagery and Video
-
Learning and Analytics in Integrated Circuits
Keywords
About this book
This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies. These disciplines are strongly related and mutually complementary; accordingly, the new series encourages an integrated approach to themes and topics in these disciplines, which will result in significant cross-fertilization, research advances and new knowledge creation. To maximize the dissemination of research findings, the series will publish edited books, monographs, handbooks, textbooks and conference proceedings. This book is intended for professors, researchers, scientists, engineers and students. An extensive list of references at the end of each chapter allows readers to probe further into those application areas that interest them most.
Editors and Affiliations
Bibliographic Information
Book Title: Machine Learning Paradigms
Book Subtitle: Applications of Learning and Analytics in Intelligent Systems
Editors: George A. Tsihrintzis, Maria Virvou, Evangelos Sakkopoulos, Lakhmi C. Jain
Series Title: Learning and Analytics in Intelligent Systems
DOI: https://doi.org/10.1007/978-3-030-15628-2
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-15627-5Published: 15 July 2019
Softcover ISBN: 978-3-030-15630-5Published: 14 August 2020
eBook ISBN: 978-3-030-15628-2Published: 06 July 2019
Series ISSN: 2662-3447
Series E-ISSN: 2662-3455
Edition Number: 1
Number of Pages: XX, 548
Number of Illustrations: 38 b/w illustrations, 101 illustrations in colour
Topics: Computational Intelligence, Data Engineering, Machine Learning