MARC details
| 000 -LEADER |
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| fixed length control field |
02450nam a2200253Ia 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
NU |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20240429134216.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230620s9999 xx 000 0 und d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
978-0-367-57061-3 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
NUFAIRVIEW |
| Transcribing agency |
NUFAIRVIEW |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
Q 325.5 A76 2019 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Arnold, Taylor |
| Relator term |
author |
| 245 #2 - TITLE STATEMENT |
|---|
| Title |
A computational approach to statistical learning / |
| Statement of responsibility, etc. |
Taylor Arnold, Michael Kane and Bryan W. Lewis |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Boca Raton, FL : |
| Name of publisher, distributor, etc. |
CRC Press, |
| Date of publication, distribution, etc. |
c2019. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xiii, 361 pages : |
| Other physical details |
illustration ; |
| Dimensions |
24 cm. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references and index. |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
<br/>Introduction<br/>Linear models<br/>Ridge regression and principal component analysis<br/>Linear smoothers<br/>Generalized linear models<br/>Additive models<br/>Penalized regression models<br/>Neural networks<br/>Dimensionality reduction<br/>Computation in practice<br/>Linear algebra and matrices<br/>Floating point arithmetic and numerical computation |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
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. -- |
| Expansion of summary note |
Provided by publisher |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
MACHINE LEARNING -- MATHEMATICS. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
MATHEMATICAL STATISTICS. ESTIMATION THEORY. |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Kane , Michael |
| Relator term |
author |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Lewis Bryan W. |
| Relator term |
author |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Koha item type |
Books |
| Suppress in OPAC |
No |
