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… (More)
Wikipedia

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
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… (More)
  • table 1
  • table 2
  • figure 1
  • table 3
  • table 4
Is this relevant?
Highly Cited
2006
Highly Cited
2006
Boosting has become very popular in computer vision, showing impressive performance in detection and recognition tasks. Mainly… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
Highly Cited
2006
Highly Cited
2006
The Maximum Margin Planning (MMP) (Ratliff et al., 2006) algorithm solves imitation learning problems by learning linear mappings… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
Is this relevant?
Highly Cited
2000
Highly Cited
2000
Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a… (More)
  • table 1
  • table 2
  • figure 1
  • figure 2
  • figure 3
Is this relevant?
Highly Cited
1999
Highly Cited
1999
Boosting is a general method for improving the accuracy of any given learning algorithm. This short paper introduces the boosting… (More)
Is this relevant?
Highly Cited
1998
Highly Cited
1998
Boosting is one of the most important recent developments in classification methodology. Boosting works by sequentially applying… (More)
  • figure I
Is this relevant?
Highly Cited
1997
Highly Cited
1997
In the rst part of the paper we consider the problem of dynamically apportioning resources among a set of options in a worst-case… (More)
Is this relevant?
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… (More)
  • figure 3
  • table 1
  • figure 4
  • figure 5
  • table 2
Is this relevant?
Highly Cited
1996
Highly Cited
1996
Breiman's bagging and Freund and Schapire's boosting are recent methods for improving the predictive power of classiier learning… (More)
Is this relevant?
Highly Cited
1996
Highly Cited
1996
Several empirical studies have connrmed that boosting class-iier-learning systems can lead to substantial improvements in… (More)
Is this relevant?