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عنوان
Supervised machine learning for text analysis in R

پدید آورنده
Emil Hvitfeldt, Julia Sigle.,Hvitfeldt, Emil,

موضوع
Computational linguistics,Natural language processing (Computer science),Supervised learning (Machine learning),Predictive analytics.,Regression analysis.,Discriminant analysis.,R (Computer program language),Statistical methods.

رده
P98
.
5
.
S83
2022

کتابخانه
Library of College of Science University of Tehran

محل استقرار
استان: Tehran ـ شهر: Tehran

Library of College of Science University of Tehran

تماس با کتابخانه : 61112616-66495290-021

INTERNATIONAL STANDARD BOOK NUMBER

(Number (ISBN
9781003093459
(Number (ISBN
9780367554187
(Number (ISBN
9780367554194

NATIONAL BIBLIOGRAPHY NUMBER

Number
E3716

LANGUAGE OF THE ITEM

.Language of Text, Soundtrack etc
انگلیسی

TITLE AND STATEMENT OF RESPONSIBILITY

Title Proper
Supervised machine learning for text analysis in R
First Statement of Responsibility
Emil Hvitfeldt, Julia Sigle.

.PUBLICATION, DISTRIBUTION, ETC

Place of Publication, Distribution, etc.
Boca Raton
Name of Publisher, Distributor, etc.
CRC Press, Taylor & Francis Group
Date of Publication, Distribution, etc.
2022.

PHYSICAL DESCRIPTION

Specific Material Designation and Extent of Item
xix, 381 p.

SERIES

Series Title
(Data science series)

GENERAL NOTES

Text of Note
"A Chapman & Hall book."

INTERNAL BIBLIOGRAPHIES/INDEXES NOTE

Text of Note
Includes bibliographical references and index.

SUMMARY OR ABSTRACT

Text of Note
"Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing"--

TOPICAL NAME USED AS SUBJECT

Entry Element
Computational linguistics
Entry Element
Natural language processing (Computer science)
Entry Element
Supervised learning (Machine learning)
Entry Element
Predictive analytics.
Entry Element
Regression analysis.
Entry Element
Discriminant analysis.
Entry Element
R (Computer program language)
Topical Subdivision
Statistical methods.

DEWEY DECIMAL CLASSIFICATION

Edition
23

LIBRARY OF CONGRESS CLASSIFICATION

Class number
P98
.
5
Book number
.
S83
2022

PERSONAL NAME - PRIMARY RESPONSIBILITY

Entry Element
Hvitfeldt, Emil,

PERSONAL NAME - ALTERNATIVE RESPONSIBILITY

Sigle, Julia,

ORIGINATING SOURCE

Country
ایران
Agency
University of Tehran. Library of College of Science
Date of Transaction
20220228141724.0
Cataloguing Rules (Descriptive Conventions))
rda

ELECTRONIC LOCATION AND ACCESS

Date and Hour of Consultation and Access
UT_SCI_BL_DB_1004005_0001.pdf

e

BL
278840

a
Y

Proposal/Bug Report

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