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Published by Taylor & Francis Ltd, 2024
ISBN 10: 0367715120 ISBN 13: 9780367715120
Language: English
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Published by Taylor & Francis Ltd, London, 2024
ISBN 10: 0367715120 ISBN 13: 9780367715120
Language: English
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Paperback. Condition: new. Paperback. With the rapidly advancing fields of Data Analytics and Computational Statistics, its important to keep up with current trends, methodologies, and applications. This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements. Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and Applications presents applications of computationally intensive methods, inference techniques, and survival analysis models. It discusses how data mining extracts information and how machine learning improves the computational model based on the new information. Those interested in this reference work will include students, professionals, and researchers working in the areas of data mining, computational statistics, operations research, and machine learning. This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Add to basketCondition: New. First edition Includes bibliographical references and index.
Published by Taylor & Francis Ltd (Sales) Okt 2024, 2024
ISBN 10: 0367715120 ISBN 13: 9780367715120
Language: English
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Add to basketTaschenbuch. Condition: Neu. Neuware - This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements.
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Published by Taylor & Francis Ltd, London, 2024
ISBN 10: 0367715120 ISBN 13: 9780367715120
Language: English
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Add to basketPaperback. Condition: new. Paperback. With the rapidly advancing fields of Data Analytics and Computational Statistics, its important to keep up with current trends, methodologies, and applications. This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements. Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and Applications presents applications of computationally intensive methods, inference techniques, and survival analysis models. It discusses how data mining extracts information and how machine learning improves the computational model based on the new information. Those interested in this reference work will include students, professionals, and researchers working in the areas of data mining, computational statistics, operations research, and machine learning. This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Published by Taylor & Francis Ltd, London, 2024
ISBN 10: 0367715120 ISBN 13: 9780367715120
Language: English
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Add to basketPaperback. Condition: new. Paperback. With the rapidly advancing fields of Data Analytics and Computational Statistics, its important to keep up with current trends, methodologies, and applications. This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements. Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and Applications presents applications of computationally intensive methods, inference techniques, and survival analysis models. It discusses how data mining extracts information and how machine learning improves the computational model based on the new information. Those interested in this reference work will include students, professionals, and researchers working in the areas of data mining, computational statistics, operations research, and machine learning. This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Published by Taylor & Francis Group, 2022
ISBN 10: 0367715112 ISBN 13: 9780367715113
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Published by Taylor & Francis Ltd, 2022
ISBN 10: 0367715112 ISBN 13: 9780367715113
Language: English
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Published by Taylor & Francis Group, 2022
ISBN 10: 0367715112 ISBN 13: 9780367715113
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ISBN 10: 0367715112 ISBN 13: 9780367715113
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Published by Springer-Verlag Gmbh, 2018
ISBN 10: 3319182838 ISBN 13: 9783319182834
Language: English
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Add to basketGebundene Ausgabe. Condition: Sehr gut. Gebraucht - Sehr gut SG - Ungelesenes Mängelexemplar, gestempelt, mit leichten Lagerspuren - Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science.
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