عنوان

A computational approach to statistical learning /

1315171406

135169474X

1351694758

1351694766

9781315171401

9781351694742

9781351694759

9781351694766

113804637X

9781138046375

A computational approach to statistical learning /

[Book]

Taylor Arnold, Michael Kane, Bryan W. Lewis.

1st

Boca Raton :

Chapman & Hall/CRC,

[2019]

©2019

1 online resource (xiii, 361 pages)

Chapman & Hall/CRC texts in statistical science series

Includes bibliographical references and index

Introduction -- Linear models -- Ridge regression and principal component analysis -- Linear smoothers -- Generalized linear models -- Additive models -- Penalized regression models -- Neural networks -- Dimensionality reduction -- Computation in practice

0

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. 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

Ingram Content Group

9781351694759

Computational approach to statistical learning.

9781138046375

Estimation theory.

Machine learning-- Mathematics.

Mathematical statistics.

BUSINESS & ECONOMICS-- Statistics.

COMPUTERS-- General.

COMPUTERS-- Machine Theory.

Estimation theory.

Mathematical statistics.

BUS-- 061000

COM-- 000000

COM-- 037000

UKC

Arnold, Taylor

Kane, Michael, (Michael John)

Lewis, Bryan W., (Bryan Wayne)

20200822160511.0

pn

[Book]

Y