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020 _a9783031204678
_9978-3-031-20467-8
024 7 _a10.1007/978-3-031-20467-8
_2doi
050 4 _aQA75.5-76.95
072 7 _aUNH
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082 0 4 _a025.04
_223
100 1 _aEsuli, Andrea.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aLearning to Quantify
_h[electronic resource] /
_cby Andrea Esuli, Alessandro Fabris, Alejandro Moreo, Fabrizio Sebastiani.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2023.
300 _aXVI, 137 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aThe Information Retrieval Series,
_x2730-6836 ;
_v47
506 0 _aOpen Access
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.
650 0 _aInformation storage and retrieval systems.
650 0 _aData mining.
650 0 _aMachine learning.
650 1 4 _aInformation Storage and Retrieval.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aMachine Learning.
700 1 _aFabris, Alessandro.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aMoreo, Alejandro.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aSebastiani, Fabrizio.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031204661
776 0 8 _iPrinted edition:
_z9783031204685
830 0 _aThe Information Retrieval Series,
_x2730-6836 ;
_v47
856 4 0 _uhttps://doi.org/10.1007/978-3-031-20467-8
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-SOB
942 _cSPRINGER
999 _c177736
_d177736