Relevant bibliographies by topics / Bayes-Lernen

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters

Academic literature on the topic 'Bayes-Lernen'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "Bayes-Lernen"

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Krüger, Dirk, and Moritz Krell. "Maschinelles Lernen mit Aussagen zur Modellkompetenz." Zeitschrift für Didaktik der Naturwissenschaften 26, no. 1 (2020): 157–72. http://dx.doi.org/10.1007/s40573-020-00118-7.

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ZusammenfassungVerfahren des maschinellen Lernens können dazu beitragen, Aussagen in Aufgaben im offenen Format in großen Stichproben zu analysieren. Am Beispiel von Aussagen von Biologielehrkräften, Biologie-Lehramtsstudierenden und Fachdidaktiker*innen zu den fünf Teilkompetenzen von Modellkompetenz (NTraining = 456; NKlassifikation = 260) wird die Qualität maschinellen Lernens mit vier Algorithmen (naïve Bayes, logistic regression, support vector machines und decision trees) untersucht. Evidenz für die Validität der Interpretation der Kodierungen einzelner Algorithmen liegt mit zufriedenste
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Dissertations / Theses on the topic "Bayes-Lernen"

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Jähnichen, Patrick. "Time Dynamic Topic Models." Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-200796.

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Information extraction from large corpora can be a useful tool for many applications in industry and academia. For instance, political communication science has just recently begun to use the opportunities that come with the availability of massive amounts of information available through the Internet and the computational tools that natural language processing can provide. We give a linguistically motivated interpretation of topic modeling, a state-of-the-art algorithm for extracting latent semantic sets of words from large text corpora, and extend this interpretation to cover issues and issu
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Müller, Christoph. "Bayesian learning in financial markets: price adjustments, fundamentals, and risk /." Münster : Verl.-Haus Monsenstein und Vannerdat, 2009. http://d-nb.info/995684332/04.

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3

Gönner, Lorenz, Julien Vitay, and Fred Hamker. "Predictive Place-Cell Sequences for Goal-Finding Emerge from Goal Memory and the Cognitive Map: A Computational Model." Universitätsbibliothek Chemnitz, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-230378.

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Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion. To address these quest
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4

Kariv, Shachar. "Theoretical and experimental essays in social learning /." 2003. http://www.gbv.de/dms/zbw/557893747.pdf.

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Jähnichen, Patrick. "Time Dynamic Topic Models." Doctoral thesis, 2015. https://ul.qucosa.de/id/qucosa%3A14614.

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Information extraction from large corpora can be a useful tool for many applications in industry and academia. For instance, political communication science has just recently begun to use the opportunities that come with the availability of massive amounts of information available through the Internet and the computational tools that natural language processing can provide. We give a linguistically motivated interpretation of topic modeling, a state-of-the-art algorithm for extracting latent semantic sets of words from large text corpora, and extend this interpretation to cover issues and issu
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6

Meder, Björn. "Seeing versus Doing: Causal Bayes Nets as Psychological Models of Causal Reasoning." Doctoral thesis, 2006. http://hdl.handle.net/11858/00-1735-0000-0006-AC65-0.

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Books on the topic "Bayes-Lernen"

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1941-, Hart Peter E., and Stork David G, eds. Pattern classification. 2nd ed. Wiley, 2001.

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2

Duda, Richard O., David G. Stork, and Peter E. Hart. Pattern Classification. Wiley & Sons, Incorporated, John, 2012.

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Duda, Richard O., David G. Stork, and Peter E. Hart. Pattern Classification. Wiley & Sons, Incorporated, John, 2009.

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Book chapters on the topic "Bayes-Lernen"

1

Rödder, W., and H. P. Reidmacher. "Bayes-Lernen in Inferenznetzwerken." In Operations Research Proceedings. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-75639-9_71.

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2

Frochte, Jörg. "Statistische Grundlagen und Bayes-Klassifikator." In Maschinelles Lernen. Carl Hanser Verlag GmbH & Co. KG, 2018. http://dx.doi.org/10.3139/9783446457058.004.

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Frochte, Jörg. "Statistische Grundlagen und Bayes-Klassifikator." In Maschinelles Lernen. Carl Hanser Verlag GmbH & Co. KG, 2019. http://dx.doi.org/10.3139/9783446459977.004.

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Frochte, Jörg. "Statistische Grundlagen und Bayes-Klassifikator." In Maschinelles Lernen. Carl Hanser Verlag GmbH & Co. KG, 2020. http://dx.doi.org/10.3139/9783446463554.004.

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5

Richter, Stefan. "Nichtparametrische Methoden und der naive Bayes-Klassifizierer." In Statistisches und maschinelles Lernen. Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-59354-7_5.

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