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Averaging Weak Classifiers

  • Dechang Chen
  • Jian Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2096)

Abstract

We present a learning algorithm for two-class pattern recognition. It is based on combining a large number of weak classifiers. The weak classifiers are produced independently with diversity. And they are combined through a weighted average, weighted exponentially with respect to their apparent errors on the training data. Experimental results are also given.

Keywords

Feature Vector Test Error Rectangular Region Weak Classifier Test Error Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Dechang Chen
    • 1
  • Jian Liu
    • 2
  1. 1.University of WisconsinGreen BayUSA
  2. 2.University of MinnesotaMinneapolisUSA

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