عنوان

An Introduction to Statistical Learnin

شابک

9781461471387

کد کشور

IR

شماره

EN-50069

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

کشور محل نشر

IR

عنوان اصلي

An Introduction to Statistical Learnin

نام عام مواد

[Book]

ساير اطلاعات عنواني

:with Applications in R

نام نخستين پديدآور

/ by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

محل نشرو پخش و غیره

New York, NY

نام ناشر، پخش کننده و غيره

: Springer New York :Imprint: Springer,

تاریخ نشرو بخش و غیره

, 2013.

نام خاص و کميت اثر

XIV, 426 p. 150 illus., 146 illus. in color., online resource.

عنوان فروست

(Springer Texts in Statistics,1431-875X

مشخصه جلد

; 103)

متن يادداشت

Print

متن يادداشت

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

متن يادداشت

Introduction -- Statistical Learning -- Linear Regression -- Classification -- Resampling Methods -- Linear Model Selection and Regularization -- Moving Beyond Linearity -- Tree-Based Methods -- Support Vector Machines -- Unsupervised Learning -- Index.?╗╣

عنوان

Springer Texts in Statistics,1431-875X

شماره جلد

103

موضوع مستند نشده

Statistics

موضوع مستند نشده

Mathematical statistics

موضوع مستند نشده

Electronic books

شماره رده

مستند نام اشخاص تاييد نشده

James, Gareth.

مستند نام اشخاص تاييد نشده

Witten, Daniela

مستند نام اشخاص تاييد نشده

Hastie, Trevor

مستند نام اشخاص تاييد نشده

Tibshirani, Robert

مستند نام اشخاص تاييد نشده

SpringerLink (Online service)

کشور

ایران

نام ميزبان

9781461471370.pdf

شماره دسترسي

عادی

اطلاعات مختصر

عادی

تاريخ و ساعت مذاکره و دسترسي

9781461471370.pdf

نوع فرمت الکترونيکي

متن

وضعیت فهرست نویسی

old catalog

فرمت انتشار

p

نوع ماده

BL

پیشوند ISBD اعمال شده است

1

سطح دسترسي

a

تكميل شده

Y