A Computational Approach to Statistical Learning (Chapman & Hall/CRC Texts in Statistical Science) - Tapa dura

Arnold, Taylor; Kane, Michael; Lewis, Bryan W.

9781138046375: A Computational Approach to Statistical Learning (Chapman & Hall/CRC Texts in Statistical Science)

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ISBN 10: 113804637X ISBN 13: 9781138046375

Editorial: Chapman and Hall/CRC, 2019

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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.

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Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015.

Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010.

Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.

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EditorialChapman and Hall/CRC
A�o de publicaci�n2019
ISBN 10 113804637X
ISBN 13 9781138046375
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IdiomaIngl�s
N�mero de edici�n1
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9780367570613: A Computational Approach to Statistical Learning (Chapman & Hall/CRC Texts in Statistical Science)

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A Computational Approach to Statistical Learning

Arnold, Taylor; Kane, Michael; Lewis, Bryan W.

Publicado por CRC Press, 2019

ISBN 10: 113804637X ISBN 13: 9781138046375

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A Computational Approach to Statistical Learning

Taylor Arnold

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A Computational Approach to Statistical Learning (Chapman & Hall/CRC Texts in Statistical Science)

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A Computational Approach to Statistical Learning (Chapman & Hall/CRC Texts in Statistical Science)

Michael Kane, Taylor Arnold, Bryan W. Lewis

Publicado por Chapman and Hall/CRC 2019-02-19, 2019

ISBN 10: 113804637X ISBN 13: 9781138046375

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Computational Approach to Statistical Learning

Arnold, Taylor; Kane, Michael; Lewis, Bryan W.

Publicado por Chapman and Hall/CRC, 2019

ISBN 10: 113804637X ISBN 13: 9781138046375

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A Computational Approach to Statistical Learning

Taylor Arnold|Michael Kane|Bryan W. Lewis

Publicado por CRC Press, 2019

ISBN 10: 113804637X ISBN 13: 9781138046375

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Condici�n: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the Nati. N� de ref. del art�culo: 273820412

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