Synopsis
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.
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About the Author
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...
From the Back Cover
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.
"About this title" may belong to another edition of this title.
- PublisherSpringer
- Publication date2020
- ISBN 10 9811510407
- ISBN 13 9789811510403
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages205
- EditorMandal Jyotsna Kumar, Mukhopadhyay Somnath, Dutta Paramartha, Dasgupta Kousik
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