Editore: Cambridge University Press, 2020
ISBN 10: 1108926207 ISBN 13: 9781108926201
Lingua: Inglese
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 29,42
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Editore: Cambridge University Press, 2020
ISBN 10: 1108926207 ISBN 13: 9781108926201
Lingua: Inglese
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 24,64
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Editore: Cambridge University Press 11/19/2020, 2020
ISBN 10: 1108926207 ISBN 13: 9781108926201
Lingua: Inglese
Da: BargainBookStores, Grand Rapids, MI, U.S.A.
EUR 24,13
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Aggiungi al carrelloPaperback or Softback. Condizione: New. Can We Be Wrong? the Problem of Textual Evidence in a Time of Data 0.28. Book.
Editore: Cambridge University Press, 2020
ISBN 10: 1108926207 ISBN 13: 9781108926201
Lingua: Inglese
Da: Kennys Bookstore, Olney, MD, U.S.A.
EUR 35,78
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Editore: Cambridge University Press, 2020
ISBN 10: 1108926207 ISBN 13: 9781108926201
Lingua: Inglese
Da: Majestic Books, Hounslow, Regno Unito
EUR 27,45
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Editore: Cambridge University Press, 2020
ISBN 10: 1108926207 ISBN 13: 9781108926201
Lingua: Inglese
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 21,80
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EUR 29,40
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Aggiungi al carrelloCondizione: New. This Element combines a machine learning-based approach to detect the prevalence and nature of generalization across tens of thousands of sentences from different disciplines alongside a robust discussion of potential solutions to the problem of the general.
Editore: Cambridge University Press, 2020
ISBN 10: 1108926207 ISBN 13: 9781108926201
Lingua: Inglese
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 24,63
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Editore: Cambridge University Press, 2020
ISBN 10: 1108926207 ISBN 13: 9781108926201
Lingua: Inglese
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 25,24
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Editore: Cambridge University Press, 2020
ISBN 10: 1108926207 ISBN 13: 9781108926201
Lingua: Inglese
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 34,14
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Editore: Cambridge University Press, 2020
ISBN 10: 1108926207 ISBN 13: 9781108926201
Lingua: Inglese
Da: Books Puddle, New York, NY, U.S.A.
EUR 35,69
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Editore: Cambridge University Press Nov 2020, 2020
ISBN 10: 1108926207 ISBN 13: 9781108926201
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 27,79
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware - This Element combines a machine learning-based approach to detect the prevalence and nature of generalization across tens of thousands of sentences from different disciplines alongside a robust discussion of potential solutions to the problem of the generalizability of textual evidence.
Editore: Cambridge University Press 2020-08-31, 2020
ISBN 10: 1108926207 ISBN 13: 9781108926201
Lingua: Inglese
Da: Chiron Media, Wallingford, Regno Unito
EUR 21,78
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Aggiungi al carrelloPaperback. Condizione: New.
Editore: Cambridge University Press, 2020
ISBN 10: 1108926207 ISBN 13: 9781108926201
Lingua: Inglese
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 28,89
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Editore: Cambridge University Press, Cambridge, 2020
ISBN 10: 1108926207 ISBN 13: 9781108926201
Lingua: Inglese
Da: CitiRetail, Stevenage, Regno Unito
EUR 30,07
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. This Element tackles the problem of generalization with respect to text-based evidence in the field of literary studies. When working with texts, how can we move, reliably and credibly, from individual observations to more general beliefs about the world? The onset of computational methods has highlighted major shortcomings of traditional approaches to texts when it comes to working with small samples of evidence. This Element combines a machine learning-based approach to detect the prevalence and nature of generalization across tens of thousands of sentences from different disciplines alongside a robust discussion of potential solutions to the problem of the generalizability of textual evidence. It exemplifies the way mixed methods can be used in complementary fashion to develop nuanced, evidence-based arguments about complex disciplinary issues in a data-driven research environment. This Element combines a machine learning-based approach to detect the prevalence and nature of generalization across tens of thousands of sentences from different disciplines alongside a robust discussion of potential solutions to the problem of the generalizability of textual evidence. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Editore: Cambridge University Press, Cambridge, 2020
ISBN 10: 1108926207 ISBN 13: 9781108926201
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 38,70
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. This Element tackles the problem of generalization with respect to text-based evidence in the field of literary studies. When working with texts, how can we move, reliably and credibly, from individual observations to more general beliefs about the world? The onset of computational methods has highlighted major shortcomings of traditional approaches to texts when it comes to working with small samples of evidence. This Element combines a machine learning-based approach to detect the prevalence and nature of generalization across tens of thousands of sentences from different disciplines alongside a robust discussion of potential solutions to the problem of the generalizability of textual evidence. It exemplifies the way mixed methods can be used in complementary fashion to develop nuanced, evidence-based arguments about complex disciplinary issues in a data-driven research environment. This Element combines a machine learning-based approach to detect the prevalence and nature of generalization across tens of thousands of sentences from different disciplines alongside a robust discussion of potential solutions to the problem of the generalizability of textual evidence. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Editore: Cambridge University Press, 2020
ISBN 10: 1108926207 ISBN 13: 9781108926201
Lingua: Inglese
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 21,78
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Editore: Cambridge University Press, Cambridge, 2020
ISBN 10: 1108926207 ISBN 13: 9781108926201
Lingua: Inglese
Da: Grand Eagle Retail, Mason, OH, U.S.A.
EUR 28,38
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. This Element tackles the problem of generalization with respect to text-based evidence in the field of literary studies. When working with texts, how can we move, reliably and credibly, from individual observations to more general beliefs about the world? The onset of computational methods has highlighted major shortcomings of traditional approaches to texts when it comes to working with small samples of evidence. This Element combines a machine learning-based approach to detect the prevalence and nature of generalization across tens of thousands of sentences from different disciplines alongside a robust discussion of potential solutions to the problem of the generalizability of textual evidence. It exemplifies the way mixed methods can be used in complementary fashion to develop nuanced, evidence-based arguments about complex disciplinary issues in a data-driven research environment. This Element combines a machine learning-based approach to detect the prevalence and nature of generalization across tens of thousands of sentences from different disciplines alongside a robust discussion of potential solutions to the problem of the generalizability of textual evidence. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Editore: Cambridge University Press, 2020
ISBN 10: 1108926207 ISBN 13: 9781108926201
Lingua: Inglese
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 25,53
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Aggiungi al carrelloPaperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 144.
Editore: Cambridge University Press, 2020
ISBN 10: 1108926207 ISBN 13: 9781108926201
Lingua: Inglese
Da: Revaluation Books, Exeter, Regno Unito
EUR 20,69
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Aggiungi al carrelloPaperback. Condizione: Brand New. 75 pages. 8.75x6.00x0.25 inches. In Stock. This item is printed on demand.