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  Projection and Projectability

Corfield, D. (2009). Projection and Projectability. In J. Quiñonero-Candela, M. Sugiyama, A. Schwaighofer, & N. Lawrence (Eds.), Dataset Shift in Machine Learning (pp. 29-38). Cambridge, MA, USA: MIT Press.

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Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-0003-09BC-0 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0003-09BD-F
Genre: Buchkapitel

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http://www.acad.bg/ebook/ml/The.MIT.Press.Dataset.Shift.in.Machine.Learning.Feb.2009.eBook-DDU.pdf (beliebiger Volltext)
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 Urheber:
Corfield, D1, 2, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Zusammenfassung: This chapter shows how the problem of dataset shift has been addressed by different philosophical schools under the concept of “projectability.” When philosophers tried to formulate scientific reasoning with the resources of predicate logic and a Bayesian inductive logic, it became evident how vital background knowledge is to allow us to project confidently into the future, or to a different place, from previous experience. To transfer expectations from one domain to another, it is important to locate robust causal mechanisms. An important debate concerning these attempts to characterize background knowledge is over whether it can all be captured by probabilistic statements. Having placed the problem within the wider philosophical perspective, the chapter turns to machine learning, and addresses a number of questions: Have machine learning theorists been sufficiently creative in their efforts to encode background knowledge? Have the frequentists been more imaginative than the Bayesians, or vice versa? Is the necessity of expressing background knowledge in a probabilistic framework too restrictive? Must relevant background knowledge be handcrafted for each application, or can it be learned?

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 Datum: 2009
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.7551/mitpress/9780262170055.001.0001
 Art des Abschluß: -

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Titel: Dataset Shift in Machine Learning
Genre der Quelle: Buch
 Urheber:
Quiñonero-Candela, J, Herausgeber
Sugiyama, M, Herausgeber
Schwaighofer, A, Herausgeber
Lawrence, ND, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: Cambridge, MA, USA : MIT Press
Seiten: - Band / Heft: - Artikelnummer: 2 Start- / Endseite: 29 - 38 Identifikator: ISBN: 978-0-262-17005-5