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Committee Machines

  • Alan Julian Izenman
Part of the Springer Texts in Statistics book series (STS)

Abstract

One of the most important research topics in machine learning is the problem of how to lower the generalization error of a learning algorithm, either by reducing the bias or the variance (or both). A major complication of any attempt to reduce variance or bias (or both) is that the definitions of “bias” and “variance” of a classification rule are not as obvious as they are in regression. In fact, there have been several conflicting suggestions for the bias-variance decomposition for classification problems.

Keywords

Loss Function Random Forest Bootstrap Sample Generalization Error AdaBoost Algorithm 
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 Science+Business Media New York 2013

Authors and Affiliations

  • Alan Julian Izenman
    • 1
  1. 1.Department of StatisticsTemple UniversityPhiladelphiaUSA

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