عنوان

A computational approach to statistical learning /

شابک

1315171406

شابک

135169474X

شابک

1351694758

شابک

1351694766

شابک

9781315171401

شابک

9781351694742

شابک

9781351694759

شابک

9781351694766

شابک اشتباه

113804637X

شابک اشتباه

9781138046375

عنوان اصلي

A computational approach to statistical learning /

نام عام مواد

[Book]

نام نخستين پديدآور

Taylor Arnold, Michael Kane, Bryan W. Lewis.

وضعيت ويراست

1st

محل نشرو پخش و غیره

Boca Raton :

نام ناشر، پخش کننده و غيره

Chapman & Hall/CRC,

تاریخ نشرو بخش و غیره

[2019]

تاریخ نشرو بخش و غیره

©2019

نام خاص و کميت اثر

1 online resource (xiii, 361 pages)

عنوان فروست

Chapman & Hall/CRC texts in statistical science series

متن يادداشت

Includes bibliographical references and index

متن يادداشت

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

بدون عنوان

0

متن يادداشت

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

منبع سفارش / آدرس اشتراک

Ingram Content Group

شماره انبار

9781351694759

عنوان

Computational approach to statistical learning.

شماره استاندارد بين المللي کتاب و موسيقي

9781138046375

موضوع مستند نشده

Estimation theory.

موضوع مستند نشده

Machine learning-- Mathematics.

موضوع مستند نشده

Mathematical statistics.

موضوع مستند نشده

BUSINESS & ECONOMICS-- Statistics.

موضوع مستند نشده

COMPUTERS-- General.

موضوع مستند نشده

COMPUTERS-- Machine Theory.

موضوع مستند نشده

Estimation theory.

موضوع مستند نشده

Mathematical statistics.

موضوع مستند نشده

BUS-- 061000

موضوع مستند نشده

COM-- 000000

موضوع مستند نشده

COM-- 037000

موضوع مستند نشده

UKC

شماره

ويراست

شماره رده

نشانه اثر

مستند نام اشخاص تاييد نشده

Arnold, Taylor

مستند نام اشخاص تاييد نشده

Kane, Michael, (Michael John)

مستند نام اشخاص تاييد نشده

Lewis, Bryan W., (Bryan Wayne)

تاريخ عمليات

20200822160511.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y