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ISBN 10: 9811510407 ISBN 13: 9789811510403
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ISBN 10: 9811510431 ISBN 13: 9789811510434
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Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
Published by Springer Nature Singapore, Springer Nature Singapore, 2020
ISBN 10: 9811510407 ISBN 13: 9789811510403
Language: English
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ISBN 10: 9811510431 ISBN 13: 9789811510434
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Add to basketTaschenbuch. Condition: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 208 pp. Englisch.
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Published by Springer Nature Singapore, 2021
ISBN 10: 9811510431 ISBN 13: 9789811510434
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Discusses machine learning applicationsIncludes a wide variety of problems using learning algorithms along with applicationsComprises chapters from experts in the field Dr. Jyotsna Kumar Mandal is a Pro.
Published by Springer Nature Singapore, 2020
ISBN 10: 9811510407 ISBN 13: 9789811510403
Language: English
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Add to basketGebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Discusses machine learning applicationsIncludes a wide variety of problems using learning algorithms along with applicationsComprises chapters from experts in the field Dr. Jyotsna Kumar Mandal is a Pro.
Published by Springer Nature Singapore Jan 2021, 2021
ISBN 10: 9811510431 ISBN 13: 9789811510434
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 208 pp. Englisch.
Published by Springer Nature Singapore Jan 2020, 2020
ISBN 10: 9811510407 ISBN 13: 9789811510403
Language: English
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Published by Springer Nature Singapore, Springer Nature Singapore Jan 2020, 2020
ISBN 10: 9811510407 ISBN 13: 9789811510403
Language: English
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Add to basketBuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 208 pp. Englisch.
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. The presents research of energy efficient accelerators for machine learning .
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