Verlag: Cambridge University Press (edition 1), 2019
ISBN 10: 110849420X ISBN 13: 9781108494205
Sprache: Englisch
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Verlag: Cambridge University Press CUP, 2019
ISBN 10: 110849420X ISBN 13: 9781108494205
Sprache: Englisch
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Verlag: Cambridge University Press, 2019
ISBN 10: 110849420X ISBN 13: 9781108494205
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Verlag: Cambridge University Press, 2019
ISBN 10: 110849420X ISBN 13: 9781108494205
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Verlag: Cambridge University Press, 2019
ISBN 10: 110849420X ISBN 13: 9781108494205
Sprache: Englisch
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In den WarenkorbZustand: New. This accessible but rigorous introduction is written for advanced undergraduates and beginning graduate students in data science, as well as researchers and practitioners. It shows how a statistical framework yields sound estimation, testing and prediction .
Verlag: Cambridge University Press 2019-09-30, 2019
ISBN 10: 110849420X ISBN 13: 9781108494205
Sprache: Englisch
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Verlag: Cambridge University Press, 2019
ISBN 10: 110849420X ISBN 13: 9781108494205
Sprache: Englisch
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In den WarenkorbBuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Colorful example-rich introduction to the state-of-the-art for students in data science, as well as researchers and practitioners.
Verlag: Cambridge University Press, 2019
ISBN 10: 110849420X ISBN 13: 9781108494205
Sprache: Englisch
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ISBN 10: 110849420X ISBN 13: 9781108494205
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ISBN 10: 110849420X ISBN 13: 9781108494205
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In den WarenkorbZustand: New. 2019. Hardcover. . . . . .
Verlag: Cambridge University Press, 2019
ISBN 10: 110849420X ISBN 13: 9781108494205
Sprache: Englisch
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ISBN 10: 110849420X ISBN 13: 9781108494205
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ISBN 10: 110849420X ISBN 13: 9781108494205
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Verlag: Cambridge University Press, Cambridge, 2019
ISBN 10: 110849420X ISBN 13: 9781108494205
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In den WarenkorbHardcover. Zustand: new. Hardcover. Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics. This accessible but rigorous introduction is written for advanced undergraduates and beginning graduate students in data science, as well as researchers and practitioners. It shows how a statistical framework yields sound estimation, testing and prediction methods, using extensive data examples and providing R code for many methods. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Verlag: Cambridge University Press, GB, 2019
ISBN 10: 110849420X ISBN 13: 9781108494205
Sprache: Englisch
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In den WarenkorbHardback. Zustand: New. Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.
Verlag: Cambridge University Press, 2019
ISBN 10: 110849420X ISBN 13: 9781108494205
Sprache: Englisch
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Verlag: Cambridge University Press, GB, 2019
ISBN 10: 110849420X ISBN 13: 9781108494205
Sprache: Englisch
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In den WarenkorbHardback. Zustand: New. Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.
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In den WarenkorbHardcover. Zustand: Brand New. 427 pages. 10.00x7.00x1.00 inches. In Stock.
Verlag: Cambridge University Press, 2019
ISBN 10: 110849420X ISBN 13: 9781108494205
Sprache: Englisch
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Verlag: Cambridge University Press, 2019
ISBN 10: 110849420X ISBN 13: 9781108494205
Sprache: Englisch
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In den Warenkorbhardcover. Zustand: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Verlag: Cambridge University Press, Cambridge, 2019
ISBN 10: 110849420X ISBN 13: 9781108494205
Sprache: Englisch
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In den WarenkorbHardcover. Zustand: new. Hardcover. Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics. This accessible but rigorous introduction is written for advanced undergraduates and beginning graduate students in data science, as well as researchers and practitioners. It shows how a statistical framework yields sound estimation, testing and prediction methods, using extensive data examples and providing R code for many methods. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Verlag: Cambridge University Press, Cambridge, 2019
ISBN 10: 110849420X ISBN 13: 9781108494205
Sprache: Englisch
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In den WarenkorbHardcover. Zustand: new. Hardcover. Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics. This accessible but rigorous introduction is written for advanced undergraduates and beginning graduate students in data science, as well as researchers and practitioners. It shows how a statistical framework yields sound estimation, testing and prediction methods, using extensive data examples and providing R code for many methods. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Verlag: Cambridge University Press
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In den Warenkorbhardcover. Zustand: Good. Page/Cover Damage, A copy that may have been read, minimal to no highlighting/underlining of text, no missing pages. May have a remainder mark. Spine may show signs of wear. Could be a library copy.
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In den WarenkorbHardcover. Zustand: Brand New. 427 pages. 10.00x7.00x1.00 inches. In Stock. This item is printed on demand.
Verlag: Cambridge University Press, 2019
ISBN 10: 110849420X ISBN 13: 9781108494205
Sprache: Englisch
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In den WarenkorbHardback. Zustand: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 1186.