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عنوان
A computational approach to statistical learning /

پدید آورنده
Taylor Arnold, Michael Kane, Bryan W. Lewis.

موضوع
Estimation theory.,Machine learning-- Mathematics.,Mathematical statistics.,BUSINESS & ECONOMICS-- Statistics.,COMPUTERS-- General.,COMPUTERS-- Machine Theory.,Estimation theory.,Mathematical statistics.

رده
Q325
.
5
.
A76
2019eb

کتابخانه
کتابخانه مطالعات اسلامی به زبان های اروپایی

محل استقرار
استان: قم ـ شهر: قم

کتابخانه مطالعات اسلامی به زبان های اروپایی

تماس با کتابخانه : 32910706-025

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

006
.
31015195
23

Q325
.
5
.
A76
2019eb

Arnold, Taylor

Kane, Michael, (Michael John)
Lewis, Bryan W., (Bryan Wayne)

20200822160511.0
pn

 مطالعه متن کتاب 

[Book]

Y

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