000			02836nam a22003735i 4500
999			_c398116 _d398116 _x1
001			398116
003			ES-MaUEC
005			20240314174528.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			230316s2023 sz | o |||| 0|eng d
020			_a9783031204678
024	7		_a10.1007/978-3-031-20467-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .D343 _b2023 EB
100	1		_aEsuli, Andrea _eautor _4http://id.loc.gov/vocabulary/relators/aut _9689472
245	1	0	_aLearning to Quantify _cby Andrea Esuli, Alessandro Fabris, Alejandro Moreo, Fabrizio Sebastiani
250			_a1st ed 2023
264		1	_aCham _bSpringer International Publishing _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aThe Information Retrieval Series _x2730-6836 _v47
520			_aThis 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.
988			_aSpringer_Computer_2023
650		7	_2embne _9162648 _aData mining
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-20467-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2024 _dz _ek _zSI