Boosting (Catalan Wikipedia)

Analysis of information sources in references of the Wikipedia article "Boosting" in Catalan language version.

Last modified: 17:35:52, 27 July 2026 (UTC)

Ref.Un. Ref.Website

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33upenn.edu

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11berkeley.edu

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11princeton.edu

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11projecteuclid.org

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11ghostarchive.org

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11mit.edu

476^th place

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berkeley.edu (Global: 621^st place; Catalan: 358^th place)

oz.berkeley.edu

Leo Breiman. «BIAS, VARIANCE, AND ARCING CLASSIFIERS». TECHNICAL REPORT, 1996. Arxivat de l'original el 2015-01-19. [Consulta: 19 gener 2015].

doi.org (Global: 2^nd place; Catalan: 4^th place)

dx.doi.org

Michael Kearns. Cryptographic limitations on learning Boolean formulae and finite automata. 21. ACM, 1989, p. 433–444. DOI 10.1145/73007.73049. ISBN 978-0897913072.
Schapire, Robert E. «Còpia arxivada». Machine Learning, 5, 2, 1990, p. 197–227. Arxivat de l'original el 2012-10-10. DOI: 10.1007/bf00116037 [Consulta: 23 agost 2012].
Leo Breiman Ann. Stat., 26, 3, 1998, p. 801–849. DOI: 10.1214/aos/1024691079 [Consulta: free]. «Schapire (1990) proved that boosting is possible. (Page 823)»

ghostarchive.org (Global: 64^th place; Catalan: 383^rd place)

Emer, Eric. «Boosting (AdaBoost algorithm)». MIT. Arxivat de l'original el 2022-10-09. [Consulta: 10 octubre 2018].

mit.edu (Global: 476^th place; Catalan: 345^th place)

math.mit.edu

Emer, Eric. «Boosting (AdaBoost algorithm)». MIT. Arxivat de l'original el 2022-10-09. [Consulta: 10 octubre 2018].

princeton.edu (Global: 802^nd place; Catalan: 714^th place)

cs.princeton.edu

Schapire, Robert E. «Còpia arxivada». Machine Learning, 5, 2, 1990, p. 197–227. Arxivat de l'original el 2012-10-10. DOI: 10.1007/bf00116037 [Consulta: 23 agost 2012].

projecteuclid.org (Global: 4,781^st place; Catalan: 3,360^th place)

Leo Breiman (1998); Arcing Classifier (with Discussion and a Rejoinder by the Author), Annals of Statistics, vol. 26, no. 3, p. 801-849: "The concept of weak learning was introduced by Kearns and Valiant (1988, 1989), who left open the question of whether weak and strong learnability are equivalent. The question was termed the boosting problem since [a solution must] boost the low accuracy of a weak learner to the high accuracy of a strong learner. Schapire (1990) proved that boosting is possible. A boosting algorithm is a method that takes a weak learner and converts it into a strong learner. Freund and Schapire (1997) proved that an algorithm similar to arc-fs is boosting.

upenn.edu (Global: 784^th place; Catalan: 715^th place)

cis.upenn.edu

Michael Kearns(1988); Thoughts on Hypothesis Boosting, Unpublished manuscript (Machine Learning class project, December 1988)
Michael Kearns(1988); Thoughts on Hypothesis Boosting, Unpublished manuscript (Machine Learning class project, December 1988)
Yoav Freund and Robert E. Schapire (1997); A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Journal of Computer and System Sciences, 55(1):119-139

web.archive.org (Global: 1^st place; Catalan: 1^st place)

Leo Breiman. «BIAS, VARIANCE, AND ARCING CLASSIFIERS». TECHNICAL REPORT, 1996. Arxivat de l'original el 2015-01-19. [Consulta: 19 gener 2015].
Schapire, Robert E. «Còpia arxivada». Machine Learning, 5, 2, 1990, p. 197–227. Arxivat de l'original el 2012-10-10. DOI: 10.1007/bf00116037 [Consulta: 23 agost 2012].

Boosting (Catalan Wikipedia)

berkeley.edu (Global: 621st place; Catalan: 358th place)

oz.berkeley.edu

doi.org (Global: 2nd place; Catalan: 4th place)

dx.doi.org

ghostarchive.org (Global: 64th place; Catalan: 383rd place)

mit.edu (Global: 476th place; Catalan: 345th place)