Algorithms in Machine Learning Paradigms
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Beschreibung
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.
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.
Discusses machine learning applications Includes a wide variety of problems using learning algorithms along with applications Comprises chapters from experts in the field
Autor*in
Jyotsna Kumar Mandal
Themen in »Algorithms in Machine Learning Paradigms«
Machine Learning Clustering Character Recognition Digital Image Processing Natural Language Processing Time Series Forecasting