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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Metrikaarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Metrika
Article . 1972 . Peer-reviewed
License: Springer TDM
Data sources: Crossref
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„Wahrscheinlichkeitslernen“ in der statistischen Lerntheorie

Authors: Feichtinger, G.;

„Wahrscheinlichkeitslernen“ in der statistischen Lerntheorie

Abstract

In diesem Beitrag zur statistischen Lerntheorie (Stimulus Sampling Theorie) wird einN-elementiges „Pattern“-Modell mitr verfugbaren Antworten und nichtkontingenter Verstarkungsvorschrift diskutiert. Um von der lerntheoretischen Literatur nicht zu sehr abhangig zu sein, wird zunachst ein kanpper Uberblick uber die Grundbegriffe der Stimulus Sampling Theorie gegeben. Der Vorgang des „Wahrscheinlichkeitslernens“ wird mittels des Reiz-Antwort-Schemas der statistischen Lerntheorie erklart. Durch die Einfuhrung der sogenannten bedingenden Zustande gelangt die Theorie derMarkovketten zur Anwendung. Schlieslich wird das Problem der Ubereinstimmung von empirischen Daten aus Lernexperimenten mit den aus der Theorie abgeleiteten anhand von Sequentialstatistiken studiert.

Country
Germany
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Keywords

510.mathematics, Article

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Top 10%
Average
Green