Published by Chapman and Hall/CRC, 2020
ISBN 10: 0367570610 ISBN 13: 9780367570613
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Published by Chapman and Hall/CRC, 2020
ISBN 10: 0367570610 ISBN 13: 9780367570613
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Published by Chapman and Hall/CRC, 2020
ISBN 10: 0367570610 ISBN 13: 9780367570613
Language: English
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Published by Chapman and Hall/CRC (edition 1), 2020
ISBN 10: 0367570610 ISBN 13: 9780367570613
Language: English
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Published by Chapman and Hall/CRC, 2020
ISBN 10: 0367570610 ISBN 13: 9780367570613
Language: English
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Published by Chapman and Hall/CRC, 2020
ISBN 10: 0367570610 ISBN 13: 9780367570613
Language: English
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Published by Taylor & Francis Group, 2020
ISBN 10: 0367570610 ISBN 13: 9780367570613
Language: English
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Published by Taylor & Francis Ltd, 2020
ISBN 10: 0367570610 ISBN 13: 9780367570613
Language: English
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Published by Chapman and Hall/CRC, 2020
ISBN 10: 0367570610 ISBN 13: 9780367570613
Language: English
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Published by Chapman and Hall/CRC 2020-06-30, 2020
ISBN 10: 0367570610 ISBN 13: 9780367570613
Language: English
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Published by Taylor & Francis Group, 2020
ISBN 10: 0367570610 ISBN 13: 9780367570613
Language: English
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Published by Chapman and Hall/CRC, 2020
ISBN 10: 0367570610 ISBN 13: 9780367570613
Language: English
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Published by Chapman and Hall/CRC, 2020
ISBN 10: 0367570610 ISBN 13: 9780367570613
Language: English
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Published by Chapman and Hall/CRC, 2019
ISBN 10: 113804637X ISBN 13: 9781138046375
Language: English
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Published by Taylor & Francis Ltd, London, 2019
ISBN 10: 113804637X ISBN 13: 9781138046375
Language: English
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Hardcover. Condition: new. Hardcover. A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset.The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Published by Chapman and Hall/CRC, 2019
ISBN 10: 113804637X ISBN 13: 9781138046375
Language: English
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Published by Taylor & Francis Group, 2019
ISBN 10: 113804637X ISBN 13: 9781138046375
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Published by Taylor & Francis Ltd, 2019
ISBN 10: 113804637X ISBN 13: 9781138046375
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Published by Taylor & Francis Group, 2019
ISBN 10: 113804637X ISBN 13: 9781138046375
Language: English
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Published by Chapman and Hall/CRC, 2019
ISBN 10: 113804637X ISBN 13: 9781138046375
Language: English
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Published by Chapman and Hall/CRC, 2019
ISBN 10: 113804637X ISBN 13: 9781138046375
Language: English
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Published by Chapman and Hall/CRC, 2019
ISBN 10: 113804637X ISBN 13: 9781138046375
Language: English
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Published by Chapman and Hall/CRC, 2019
ISBN 10: 113804637X ISBN 13: 9781138046375
Language: English
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Published by CRC Press, Boco Raton ; London ; New York, 2019
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Add to basketCondition: Fine. Hardcover : pp. [vi] xiii [xiv] 361 [1 blank] : graphs, models and figures : red cover with graph in white.
Published by Taylor & Francis Group, 2019
ISBN 10: 113804637X ISBN 13: 9781138046375
Language: English
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Published by Chapman and Hall/CRC, 2019
ISBN 10: 113804637X ISBN 13: 9781138046375
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Published by Taylor & Francis Ltd, London, 2019
ISBN 10: 113804637X ISBN 13: 9781138046375
Language: English
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Add to basketHardcover. Condition: new. Hardcover. A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset.The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Add to basketHardcover. Condition: Brand New. 361 pages. 9.25x6.25x1.00 inches. In Stock.
Published by Chapman and Hall/CRC, 2019
ISBN 10: 113804637X ISBN 13: 9781138046375
Language: English
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