Robust Recognition Based on Adaptive Combination of Weak Classifiers

Guoping Wang, Misha Pavel, Xubo Song

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations


We describe a novel adaptive method that achieves robustness in pattern classification by combining a large number of weak classifiers. The individual classifiers are trained on subsets of features of the training samples and the output classification is obtained by a weighted sum of the individual weak classifiers. When the classifier is applied to the test set, the combination weights are adaptively adjusted in accordance with the agreement among the individual classifiers. We evaluated the performances of several different combination methods using simulated data and the results proved to be robust.

Original languageEnglish (US)
Number of pages5
StatePublished - 2003
EventInternational Joint Conference on Neural Networks 2003 - Portland, OR, United States
Duration: Jul 20 2003Jul 24 2003


OtherInternational Joint Conference on Neural Networks 2003
Country/TerritoryUnited States
CityPortland, OR

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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