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Boosting (machine learning)

Known as: Boost, Boosting (meta-algorithm), Boosting methods for object categorization 
Boosting is a machine learning ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine… Expand
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Papers overview

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Highly Cited
2010
Highly Cited
2010
Transfer learning allows leveraging the knowledge of source domains, available a priori, to help training a classifier for a… Expand
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Highly Cited
2009
Highly Cited
2009
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of… Expand
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Highly Cited
2007
Highly Cited
2007
Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However… Expand
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Highly Cited
2004
Highly Cited
2004
Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a… Expand
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Highly Cited
2001
Highly Cited
2001
We have constructed a frontal face detection system which achieves detection and false positive rates which are equivalent to the… Expand
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Highly Cited
2000
Highly Cited
2000
This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks. Our… Expand
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Highly Cited
2000
Highly Cited
2000
The main and important contribution of this paper is in establishing a connection between boosting, a newcomer to the statistics… Expand
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Highly Cited
1997
Highly Cited
1997
In the first part of the paper we consider the problem of dynamically apportioning resources among a set of options in a worst… Expand
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Highly Cited
1996
Highly Cited
1996
In an earlier paper, we introduced a new "boosting" algorithm called AdaBoost which, theoretically, can be used to significantly… Expand
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Highly Cited
1995
Highly Cited
1995
In the first part of the paper we consider the problem of dynamically apportioning resources among a set of options in a worst… Expand
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