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A computational approach to statisti...

Arnold, Taylor,

A computational approach to statistical learning /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	A computational approach to statistical learning /Taylor Arnold, Michael Kane, Bryan W. Lewis.
作者:	Arnold, Taylor,
其他作者:	Kane, Michael
出版者:	Boca Raton, FL :CRC Press, Taylor & Francis Group,c2019.
面頁冊數:	xiii, 361 p. :ill. ;25 cm.
標題:	Machine learningMathematics.
ISBN:	9781138046375 :
ISSN:	cam a2200277 a 450

A computational approach to statistical learning /
Arnold, Taylor,

A computational approach to statistical learning /Taylor Arnold, Michael Kane, Bryan W. Lewis. - Boca Raton, FL :CRC Press, Taylor & Francis Group,c2019. - xiii, 361 p. :ill. ;25 cm. - Chapman & Hall/CRC texts in statistical science series. - Texts in statistical science..

Includes bibliographical references and index.

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. --

ISBN: 9781138046375 :GBP$65

ISSN: cam a2200277 a 450

LCCN: 2018049270

Nat. Bib. No.: GBB8M6332bnb

Nat. Bib. Agency Control No.: 019157667UkSubjects--Topical Terms:

857106
Machine learning
--Mathematics.

LC Class. No.: Q325.5 / .A76 2019

Dewey Class. No.: 006.3/1015195

A computational approach to statistical learning /
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300 $a xiii, 361 p. : $b ill. ; $c 25 cm.
490 1 $a Chapman & Hall/CRC texts in statistical science series
504 $a Includes bibliographical references and index.
520 $a 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. -- $c Provided by publisher.
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650 0 $a Estimation theory. $3 181864
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700 1 $a Lewis, Bryan W. $q (Bryan Wayne), $e author. $3 857958
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