Learning to Quantify
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...
में बचाया:
| मुख्य लेखकों: | , , , |
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| स्वरूप: | Online |
| भाषा: | अंग्रेज़ी |
| प्रकाशित: |
Springer Nature
2023
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| विषय: | |
| ऑनलाइन पहुंच: | ONIX_20230413_9783031204678_16 |
| टैग: |
कोई टैग नहीं, इस रिकॉर्ड को टैग करने वाले पहले व्यक्ति बनें!
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| _version_ | 1869513816555913216 |
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| author | Esuli, Andrea Fabris, Alessandro Moreo, Alejandro Sebastiani, Fabrizio |
| author_browse | Esuli, Andrea Fabris, Alessandro Moreo, Alejandro Sebastiani, Fabrizio |
| author_facet | Esuli, Andrea Fabris, Alessandro Moreo, Alejandro Sebastiani, Fabrizio |
| author_sort | Esuli, Andrea |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-99160 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Springer Nature |
| publisherStr | Springer Nature |
| record_format | ojs |
| spelling | doab-20.500.12854ir-991602024-04-14T10:27:52Z Learning to Quantify Esuli, Andrea Fabris, Alessandro Moreo, Alejandro Sebastiani, Fabrizio Information Retrieval Machine Learning Supervised Learning Data Mining Prevalence Estimation Class Prior Estimation Data Science thema EDItEUR::U Computing and Information Technology::UN Databases::UNH Information retrieval thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UN Databases::UNH Information retrieval thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning 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. 2023-04-18T09:55:12Z 2023-04-18T09:55:12Z 2023-04-13T14:03:24Z 2023 book ONIX_20230413_9783031204678_16 https://library.oapen.org/handle/20.500.12657/62385 9783031204678 9783031204661 https://directory.doabooks.org/handle/20.500.12854/99160 eng The Information Retrieval Series open access image/jpeg image/jpeg n/a n/a https://library.oapen.org/bitstream/20.500.12657/62385/1/978-3-031-20467-8.pdf https://library.oapen.org/bitstream/20.500.12657/62385/1/978-3-031-20467-8.pdf Springer Nature Springer International Publishing 10.1007/978-3-031-20467-8 10.1007/978-3-031-20467-8 9fa3421d-f917-4153-b9ab-fc337c396b5a Istituto di Scienza e Tecnologie dell'Informazione 9783031204678 9783031204661 Springer International Publishing 137 Cham open access |
| spellingShingle | Information Retrieval Machine Learning Supervised Learning Data Mining Prevalence Estimation Class Prior Estimation Data Science thema EDItEUR::U Computing and Information Technology::UN Databases::UNH Information retrieval thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UN Databases::UNH Information retrieval thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning Esuli, Andrea Fabris, Alessandro Moreo, Alejandro Sebastiani, Fabrizio Learning to Quantify |
| title | Learning to Quantify |
| title_full | Learning to Quantify |
| title_fullStr | Learning to Quantify |
| title_full_unstemmed | Learning to Quantify |
| title_short | Learning to Quantify |
| title_sort | learning to quantify |
| topic | Information Retrieval Machine Learning Supervised Learning Data Mining Prevalence Estimation Class Prior Estimation Data Science thema EDItEUR::U Computing and Information Technology::UN Databases::UNH Information retrieval thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UN Databases::UNH Information retrieval thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| topic_facet | Information Retrieval Machine Learning Supervised Learning Data Mining Prevalence Estimation Class Prior Estimation Data Science thema EDItEUR::U Computing and Information Technology::UN Databases::UNH Information retrieval thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UN Databases::UNH Information retrieval thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| url | ONIX_20230413_9783031204678_16 |
| work_keys_str_mv | AT esuliandrea learningtoquantify AT fabrisalessandro learningtoquantify AT moreoalejandro learningtoquantify AT sebastianifabrizio learningtoquantify |