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Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2023
ISBN 10: 3031204662 ISBN 13: 9783031204661
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Aggiungi al carrelloPaperback. Condizione: New. 1st ed. 2023. 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.
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Lingua: Inglese
Editore: Springer-Nature New York Inc, 2023
ISBN 10: 3031204662 ISBN 13: 9783031204661
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Editore: Springer, Berlin|Springer International Publishing|Istituto di Scienza e Tecnologie dell'Informazione|Springer, 2023
ISBN 10: 3031204662 ISBN 13: 9783031204661
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Aggiungi al carrelloKartoniert / Broschiert. Condizione: New. 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 qu.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Learning to Quantify | Andrea Esuli (u. a.) | Taschenbuch | The Information Retrieval Series | xvi | Englisch | 2023 | Springer | EAN 9783031204661 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2023
ISBN 10: 3031204662 ISBN 13: 9783031204661
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Aggiungi al carrelloPaperback. Condizione: New. 1st ed. 2023. 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.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Automatic Generation of Lexical Resources for Opinion Mining | Models, Algorithms and Applications | Andrea Esuli | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783836473330 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Aggiungi al carrelloPAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Lingua: Inglese
Editore: Springer-Nature New York Inc, 2023
ISBN 10: 3031204662 ISBN 13: 9783031204661
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Aggiungi al carrelloPaperback. Condizione: Brand New. 153 pages. 9.25x6.10x0.33 inches. In Stock. This item is printed on demand.
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Editore: Springer International Publishing Mrz 2023, 2023
ISBN 10: 3031204662 ISBN 13: 9783031204661
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 156 pp. Englisch.
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND.
Lingua: Inglese
Editore: Springer, Palgrave Macmillan Mär 2023, 2023
ISBN 10: 3031204662 ISBN 13: 9783031204661
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 (¿macrö) data rather than on individual (¿micrö) data.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 156 pp. Englisch.
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Esuli AndreaAndrea Esuli is a Researcher at the Institute of Information Science and Technologies, an institute of the Italian National Research Council. His primary areas of research are text classification, information extraction, .