Learning to Quantify (Record no. 177736)

MARC details
000 -LEADER
fixed length control field 03666nam a22006015i 4500
001 - CONTROL NUMBER
control field 978-3-031-20467-8
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240423125423.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 230316s2023 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783031204678
-- 978-3-031-20467-8
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-3-031-20467-8
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA75.5-76.95
072 #7 - SUBJECT CATEGORY CODE
Subject category code UNH
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code UND
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM030000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code UNH
Source thema
072 #7 - SUBJECT CATEGORY CODE
Subject category code UND
Source thema
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 025.04
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Esuli, Andrea.
Relator term author.
Relator code aut
-- http://id.loc.gov/vocabulary/relators/aut
245 10 - TITLE STATEMENT
Title Learning to Quantify
Medium [electronic resource] /
Statement of responsibility, etc by Andrea Esuli, Alessandro Fabris, Alejandro Moreo, Fabrizio Sebastiani.
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2023.
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2023.
300 ## - PHYSICAL DESCRIPTION
Extent XVI, 137 p. 1 illus.
Other physical details online resource.
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-- computer
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-- online resource
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490 1# - SERIES STATEMENT
Series statement The Information Retrieval Series,
International Standard Serial Number 2730-6836 ;
Volume number/sequential designation 47
506 0# - RESTRICTIONS ON ACCESS NOTE
Terms governing access Open Access
520 ## - SUMMARY, ETC.
Summary, etc This open access book provides an introduction and an overview of learning to quantify (a.k.a. “quantification”), i.e. the task of training estimators of class proportions in unlabeled data by means of supervised learning. In data science, learning to quantify is a task of its own related to classification yet different from it, since estimating class proportions by simply classifying all data and counting the labels assigned by the classifier is known to often return inaccurate (“biased”) class proportion estimates. The book introduces learning to quantify by looking at the supervised learning methods that can be used to perform it, at the evaluation measures and evaluation protocols that should be used for evaluating the quality of the returned predictions, at the numerous fields of human activity in which the use of quantification techniques may provide improved results with respect to the naive use of classification techniques, and at advanced topics in quantification research. The book is suitable to researchers, data scientists, or PhD students, who want to come up to speed with the state of the art in learning to quantify, but also to researchers wishing to apply data science technologies to fields of human activity (e.g., the social sciences, political science, epidemiology, market research) which focus on aggregate (“macro”) data rather than on individual (“micro”) data.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Information storage and retrieval systems.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data mining.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Information Storage and Retrieval.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data Mining and Knowledge Discovery.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine Learning.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Fabris, Alessandro.
Relator term author.
Relator code aut
-- http://id.loc.gov/vocabulary/relators/aut
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Moreo, Alejandro.
Relator term author.
Relator code aut
-- http://id.loc.gov/vocabulary/relators/aut
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Sebastiani, Fabrizio.
Relator term author.
Relator code aut
-- http://id.loc.gov/vocabulary/relators/aut
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer Nature eBook
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783031204661
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783031204685
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title The Information Retrieval Series,
-- 2730-6836 ;
Volume number/sequential designation 47
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/978-3-031-20467-8">https://doi.org/10.1007/978-3-031-20467-8</a>
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942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks-CSE-Springer

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