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- Boosting_(machine_learning) abstract "Boosting is a machine learning ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms which convert weak learners to strong ones. Boosting is based on the question posed by Kearns and Valiant (1988, 1989): Can a set of weak learners create a single strong learner? A weak learner is defined to be a classifier which is only slightly correlated with the true classification (it can label examples better than random guessing). In contrast, a strong learner is a classifier that is arbitrarily well-correlated with the true classification.Robert Schapire's affirmative answer in a 1990 paper to the question of Kearns and Valiant has had significant ramifications in machine learning and statistics, most notably leading to the development of boosting.When first introduced, the hypothesis boosting problem simply referred to the process of turning a weak learner into a strong learner. \"Informally, [the hypothesis boosting] problem asks whether an efficient learning algorithm […] that outputs a hypothesis whose performance is only slightly better than random guessing [i.e. a weak learner] implies the existence of an efficient algorithm that outputs a hypothesis of arbitrary accuracy [i.e. a strong learner].\" Algorithms that achieve hypothesis boosting quickly became simply known as \"boosting\". Freund and Schapire's arcing (Adapt[at]ive Resampling and Combining), as a general technique, is more or less synonymous with boosting.".
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- Boosting_(machine_learning) wikiPageExternalLink Schapire2003.pdf.
- Boosting_(machine_learning) wikiPageExternalLink adaboost.pdf.
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- Boosting_(machine_learning) wikiPageWikiLink Bootstrap_aggregating.
- Boosting_(machine_learning) wikiPageWikiLink BrownBoost.
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- Boosting_(machine_learning) wikiPageWikiLink Category:Classification_algorithms.
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- Boosting_(machine_learning) wikiPageWikiLink Principle_of_maximum_entropy.
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- Boosting_(machine_learning) wikiPageWikiLink Support_vector_machine.
- Boosting_(machine_learning) wikiPageWikiLink TotalBoost.
- Boosting_(machine_learning) wikiPageWikiLink Weka_(machine_learning).
- Boosting_(machine_learning) wikiPageWikiLink Yoav_Freund.
- Boosting_(machine_learning) wikiPageWikiLink Zhou_Zhihua.
- Boosting_(machine_learning) wikiPageWikiLinkText "Boosting (machine learning)".
- Boosting_(machine_learning) wikiPageWikiLinkText "Boosting".
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- Boosting_(machine_learning) wikiPageWikiLinkText "boosting algorithms".
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- Boosting_(machine_learning) subject Category:Classification_algorithms.
- Boosting_(machine_learning) subject Category:Ensemble_learning.
- Boosting_(machine_learning) hypernym Machine.
- Boosting_(machine_learning) type Software.
- Boosting_(machine_learning) type Algorithm.
- Boosting_(machine_learning) comment "Boosting is a machine learning ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms which convert weak learners to strong ones.".
- Boosting_(machine_learning) label "Boosting (machine learning)".
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