000 | 03666nam a22006015i 4500 | ||
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001 | 978-3-031-20467-8 | ||
003 | DE-He213 | ||
005 | 20240423125423.0 | ||
007 | cr nn 008mamaa | ||
008 | 230316s2023 sz | s |||| 0|eng d | ||
020 |
_a9783031204678 _9978-3-031-20467-8 |
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024 | 7 |
_a10.1007/978-3-031-20467-8 _2doi |
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050 | 4 | _aQA75.5-76.95 | |
072 | 7 |
_aUNH _2bicssc |
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_aUND _2bicssc |
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_aCOM030000 _2bisacsh |
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_aUNH _2thema |
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_aUND _2thema |
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082 | 0 | 4 |
_a025.04 _223 |
100 | 1 |
_aEsuli, Andrea. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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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. |
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300 |
_aXVI, 137 p. 1 illus. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aThe Information Retrieval Series, _x2730-6836 ; _v47 |
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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 |
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700 | 1 |
_aMoreo, Alejandro. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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700 | 1 |
_aSebastiani, Fabrizio. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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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 |