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The Bias Variance Trade-Off in Bootstrapped Error Correcting Output Code Ensembles

  • Raymond S. Smith
  • Terry Windeatt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5519)

Abstract

By performing experiments on publicly available multi-class datasets we examine the effect of bootstrapping on the bias/variance behaviour of error-correcting output code ensembles. We present evidence to show that the general trend is for bootstrapping to reduce variance but to slightly increase bias error. This generally leads to an improvement in the lowest attainable ensemble error, however this is not always the case and bootstrapping appears to be most useful on datasets where the non-bootstrapped ensemble classifier is prone to overfitting.

Keywords

Support Vector Machine Training Strength Relative Percentage Change Bootstrap Error Ensemble Error 
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 2009

Authors and Affiliations

  • Raymond S. Smith
    • 1
  • Terry Windeatt
    • 1
  1. 1.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyGuildfordUK

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