Verzerrung-Varianz-Dilemma (German Wikipedia)

Analysis of information sources in references of the Wikipedia article "Verzerrung-Varianz-Dilemma" in German language version.

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3doi.org

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2stanford.edu

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1mit.edu

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1usc.edu

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1cornell.edu

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1rasbt.github.io

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1ed.ac.uk

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1web.archive.org

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1redirecter.toolforge.org

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1washington.edu

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1fortmann-roe.com

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1nih.gov

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cornell.edu

cs.cornell.edu

https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote12.html

doi.org

Stuart Geman, E. Bienenstock, R. Doursat: Neural networks and the bias/variance dilemma. In: Neural Computation. 4. Jahrgang, 1992, S. 1–58, doi:10.1162/neco.1992.4.1.1 (mit.edu [PDF]).
F. Gagliardi: Instance-based classifiers applied to medical databases: diagnosis and knowledge extraction. Artificial Intelligence in Medicine. Bande 52, Nr. 3 (2011), S. 123–139. Doi:10.1016/j.artmed.2011.04.002
Gerd Gigerenzer, Henry Brighton: Homo heuristicus: Why biased minds make better inferences. In: Topics in Cognitive Science. Band 1, Nr. 1, Januar 2009, S. 107–143, doi:10.1111/j.1756-8765.2008.01006.x, PMID 25164802 (englisch).

ed.ac.uk

inf.ed.ac.uk

Sethu Vijayakumar: The Bias–Variance Tradeoff. University Edinburgh, 2007, abgerufen am 19. August 2014.

fortmann-roe.com

scott.fortmann-roe.com

Scott Fortmann-Roe: Understanding the Bias–Variance Tradeoff. 2012. [1]

mit.edu

web.mit.edu

Stuart Geman, E. Bienenstock, R. Doursat: Neural networks and the bias/variance dilemma. In: Neural Computation. 4. Jahrgang, 1992, S. 1–58, doi:10.1162/neco.1992.4.1.1 (mit.edu [PDF]).

nih.gov

ncbi.nlm.nih.gov

Gerd Gigerenzer, Henry Brighton: Homo heuristicus: Why biased minds make better inferences. In: Topics in Cognitive Science. Band 1, Nr. 1, Januar 2009, S. 107–143, doi:10.1111/j.1756-8765.2008.01006.x, PMID 25164802 (englisch).

rasbt.github.io

Durch Monte-Carlo-Simulationen können neue Trainingsdaten (Stichprobenwiderholungen) mit dem Bootstrapping-Verfahren erzeugt werden, die es dann erlauben den Erwartungswert $\mathrm {E}$ numerisch zu schätzen, vgl. http://rasbt.github.io/mlxtend/user_guide/evaluate/bias_variance_decomp/

redirecter.toolforge.org

Greg Shakhnarovich: Notes on derivation of bias-variance decomposition in linear regression. 2011, archiviert vom Original am 21. August 2014; abgerufen am 20. August 2014.

stanford.edu

statweb.stanford.edu

Trevor Hastie, Robert Tibshirani, JeromeFriedman: The Elements of Statistical Learning. 2009 (online).

nlp.stanford.edu

Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze: Introduction to Information Retrieval. Cambridge University Press, 2008, S. 308–314 (online).

usc.edu

www-bcf.usc.edu

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani: An Introduction to Statistical Learning. Springer, 2013 (online).

washington.edu

homes.cs.washington.edu

Pedro Domingos: A unified bias-variance decomposition. ICML. 2000 (englisch, washington.edu [PDF]).

web.archive.org

Greg Shakhnarovich: Notes on derivation of bias-variance decomposition in linear regression. 2011, archiviert vom Original am 21. August 2014; abgerufen am 20. August 2014.