Learning to Quantify / by Andrea Esuli, Alessandro Fabris, Alejandro Moreo, Fabrizio Sebastiani.
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 classif...
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Superior document: | The Information Retrieval Series, 47 |
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Place / Publishing House: | Cham : : Springer International Publishing :, Imprint: Springer,, 2023. |
Year of Publication: | 2023 |
Edition: | 1st ed. 2023. |
Language: | English |
Series: | The Information Retrieval Series,
47 |
Physical Description: | 1 online resource (XVI, 137 p. 1 illus.) |
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Esuli, Andrea. author. aut http://id.loc.gov/vocabulary/relators/aut Learning to Quantify [electronic resource] / by Andrea Esuli, Alessandro Fabris, Alejandro Moreo, Fabrizio Sebastiani. 1st ed. 2023. Cham : Springer International Publishing : Imprint: Springer, 2023. 1 online resource (XVI, 137 p. 1 illus.) text txt rdacontent computer c rdamedia online resource cr rdacarrier The Information Retrieval Series, 2730-6836 ; 47 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. Open Access Includes bibliographical references and index. - 1. The Case for Quantification. - 2. Applications of Quantification. - 3. Evaluation of Quantification Algorithms. - 4. Methods for Learning to Quantify. - 5. Advanced Topics. - 6. The Quantification Landscape. - 7. The Road Ahead. Information storage and retrieval systems. Data mining. Machine learning. Information Storage and Retrieval. Data Mining and Knowledge Discovery. Machine Learning. 3-031-20466-2 Fabris, Alessandro. author. aut http://id.loc.gov/vocabulary/relators/aut Moreo, Alejandro. author. aut http://id.loc.gov/vocabulary/relators/aut Sebastiani, Fabrizio. author. (orcid)0000-0003-4221-6427 https://orcid.org/0000-0003-4221-6427 aut http://id.loc.gov/vocabulary/relators/aut |
language |
English |
format |
Electronic eBook |
author |
Esuli, Andrea. Esuli, Andrea. Fabris, Alessandro. Moreo, Alejandro. Sebastiani, Fabrizio. |
spellingShingle |
Esuli, Andrea. Esuli, Andrea. Fabris, Alessandro. Moreo, Alejandro. Sebastiani, Fabrizio. Learning to Quantify The Information Retrieval Series, - 1. The Case for Quantification. - 2. Applications of Quantification. - 3. Evaluation of Quantification Algorithms. - 4. Methods for Learning to Quantify. - 5. Advanced Topics. - 6. The Quantification Landscape. - 7. The Road Ahead. |
author_facet |
Esuli, Andrea. Esuli, Andrea. Fabris, Alessandro. Moreo, Alejandro. Sebastiani, Fabrizio. Fabris, Alessandro. Fabris, Alessandro. Moreo, Alejandro. Moreo, Alejandro. Sebastiani, Fabrizio. Sebastiani, Fabrizio. |
author_variant |
a e ae a e ae a f af a m am f s fs |
author_role |
VerfasserIn VerfasserIn VerfasserIn VerfasserIn VerfasserIn |
author2 |
Fabris, Alessandro. Fabris, Alessandro. Moreo, Alejandro. Moreo, Alejandro. Sebastiani, Fabrizio. Sebastiani, Fabrizio. |
author2_variant |
a f af a m am f s fs |
author2_role |
VerfasserIn VerfasserIn VerfasserIn VerfasserIn VerfasserIn VerfasserIn |
author_sort |
Esuli, Andrea. |
title |
Learning to Quantify |
title_full |
Learning to Quantify [electronic resource] / by Andrea Esuli, Alessandro Fabris, Alejandro Moreo, Fabrizio Sebastiani. |
title_fullStr |
Learning to Quantify [electronic resource] / by Andrea Esuli, Alessandro Fabris, Alejandro Moreo, Fabrizio Sebastiani. |
title_full_unstemmed |
Learning to Quantify [electronic resource] / by Andrea Esuli, Alessandro Fabris, Alejandro Moreo, Fabrizio Sebastiani. |
title_auth |
Learning to Quantify |
title_new |
Learning to Quantify |
title_sort |
learning to quantify |
series |
The Information Retrieval Series, |
series2 |
The Information Retrieval Series, |
publisher |
Springer International Publishing : Imprint: Springer, |
publishDate |
2023 |
physical |
1 online resource (XVI, 137 p. 1 illus.) |
edition |
1st ed. 2023. |
contents |
- 1. The Case for Quantification. - 2. Applications of Quantification. - 3. Evaluation of Quantification Algorithms. - 4. Methods for Learning to Quantify. - 5. Advanced Topics. - 6. The Quantification Landscape. - 7. The Road Ahead. |
isbn |
3-031-20467-0 3-031-20466-2 |
issn |
2730-6836 ; |
callnumber-first |
Q - Science |
callnumber-subject |
QA - Mathematics |
callnumber-label |
QA75 |
callnumber-sort |
QA 275.5 276.95 |
illustrated |
Not Illustrated |
dewey-hundreds |
000 - Computer science, information & general works |
dewey-tens |
020 - Library & information sciences |
dewey-ones |
025 - Library operations |
dewey-full |
025.04 |
dewey-sort |
225.04 |
dewey-raw |
025.04 |
dewey-search |
025.04 |
oclc_num |
1373612276 |
work_keys_str_mv |
AT esuliandrea learningtoquantify AT fabrisalessandro learningtoquantify AT moreoalejandro learningtoquantify AT sebastianifabrizio learningtoquantify |
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hierarchy_parent_title |
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hierarchy_sequence |
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is_hierarchy_title |
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container_title |
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