عنوان

A computational approach to statistical learning /

(Number (ISBN

1315171406

(Number (ISBN

135169474X

(Number (ISBN

1351694758

(Number (ISBN

1351694766

(Number (ISBN

9781315171401

(Number (ISBN

9781351694742

(Number (ISBN

9781351694759

(Number (ISBN

9781351694766

Erroneous ISBN

113804637X

Erroneous ISBN

9781138046375

Title Proper

A computational approach to statistical learning /

General Material Designation

[Book]

First Statement of Responsibility

Taylor Arnold, Michael Kane, Bryan W. Lewis.

Edition Statement

1st

Place of Publication, Distribution, etc.

Boca Raton :

Name of Publisher, Distributor, etc.

Chapman & Hall/CRC,

Date of Publication, Distribution, etc.

[2019]

Date of Publication, Distribution, etc.

©2019

Specific Material Designation and Extent of Item

1 online resource (xiii, 361 pages)

Series Title

Chapman & Hall/CRC texts in statistical science series

Text of Note

Includes bibliographical references and index

Text of Note

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

Text of Note

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

Source for Acquisition/Subscription Address

Ingram Content Group

Stock Number

9781351694759

Title

Computational approach to statistical learning.

International Standard Book Number

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

Number

Edition

Class number

Book number

Arnold, Taylor

Kane, Michael, (Michael John)

Lewis, Bryan W., (Bryan Wayne)

Date of Transaction

20200822160511.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y