Overview
- Discusses machine learning applications
- Includes a wide variety of problems using learning algorithms along with applications
- Comprises chapters from experts in the field
Part of the book series: Studies in Computational Intelligence (SCI, volume 870)
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Table of contents (11 chapters)
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Front Matter
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Back Matter
Keywords
About this book
This book presents studies involving algorithms in the machine learning paradigms. It discusses a variety of learning problems with diverse applications, including prediction, concept learning, explanation-based learning, case-based (exemplar-based) learning, statistical rule-based learning, feature extraction-based learning, optimization-based learning, quantum-inspired learning, multi-criteria-based learning and hybrid intelligence-based learning.
Editors and Affiliations
About the editors
Dr. Jyotsna Kumar Mandal is a Professor of Computer Science & Engineering, and former Dean of FETM, Kalyani University, India. He holds an M.Sc. in Physics from Jadavpur University, M. Tech. in Computer Science from the University of Calcutta, and was awarded a Ph.D. in Computer Science & Engineering by Jadavpur University. He has 32 years of teaching and research experience in various fields of computer science and allied areas, and has published 170 articles in journals, more than 300 articles at conferences, and edited 31 volumes and seven books. He is a Fellow of IETE, life member of CRSI and CSI, and senior member of IEEE.
Dr. Somnath Mukhopadhyay is an Assistant Professor at the Department of Computer Science and Engineering, Assam University, Silchar, India. He completed his M.Tech. and Ph.D. degrees in Computer Science and Engineering at the University of Kalyani, India. He has co-authored one book and edited six books and published over 25 papers in various international journals and conference proceedings, including three chapters in edited volumes. His research interests include remote sensing and computational intelligence. He is a life member of the Computer Society of India and currently the Regional Student Coordinator of Region II, Computer Society of India.
Prof. (Dr.) Paramartha Dutta, FIE (India), FIETE, FOSI, SMIEEE, SMACM, SMCSI, completed his bachelor’s and master’s in Statistics and Master of Technology in Computer Science at the Indian Statistical Institute, and his Ph.D. in Engineering at the Bengal Engineering and Science University, Shibpur. He has co-authored eight books and twelve edited books, and published 250 papers in various peer-reviewed journals and conference proceedings as well as several book chapters. He holds six international and eleven national patents.
Dr. Dutta received (i) IRDP Lifetime Achievement Award 2018, (ii) Distinguished Scientist Award in Computer Science and Engineering 2018 conferred by the Venus International Research Foundation, (iii) Excellence in Science and Technology Award 2018 – 2019 conferred by the Indian Science Congress Association, Government of India, and (iv) INSA Teacher Award 2019 conferred by the Indian National Science Academy, Government of India.
Dr. Kousik Dasgupta is an Assistant Professor of Computer Science & Engineering, Kalyani Government Engineering College, West Bengal. He completed his B.Tech. in Electronics and Power Engineering at Nagpur University in 1993, M.Tech. in Computer Science at West Bengal University of Technology in 2007, and his Ph.D. at the Department of Computer Science and Engineering, University of Kalyani, in 2017. Dr. Dasgupta has presented numerous papers at various conferences and co-authored several books. He is a member of five scientific and professional societies.
Bibliographic Information
Book Title: Algorithms in Machine Learning Paradigms
Editors: Jyotsna Kumar Mandal, Somnath Mukhopadhyay, Paramartha Dutta, Kousik Dasgupta
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-981-15-1041-0
Publisher: Springer Singapore
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2020
Hardcover ISBN: 978-981-15-1040-3Published: 04 January 2020
Softcover ISBN: 978-981-15-1043-4Published: 04 January 2021
eBook ISBN: 978-981-15-1041-0Published: 03 January 2020
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: X, 195
Number of Illustrations: 46 b/w illustrations, 69 illustrations in colour
Topics: Mathematical and Computational Engineering, Machine Learning, Image Processing and Computer Vision, Natural Language Processing (NLP), Signal, Image and Speech Processing