ML Anthology

Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example

Günther Eibl, Karl Peter Pfeiffer
ECML-PKDD 2001 pp. 109-120
doi:10.1007/3-540-44795-4_10 /ecmlpkdd/2001/eibl2001ecml-analysis/

Abstract

In simulation studies boosting algorithms seem to be susceptible to noise. This article applies Ada. Boost. M2 used with decision stumps to the digit recognition example, a simulated data set with attribute noise. Although the final model is both simple and complex enough, boosting fails to reach the Bayes error. A detailed analysis shows some characteristics of the boosting trials which influence the lack of fit.

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Cite

Text

Eibl and Pfeiffer. "Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example." European Conference on Machine Learning, 2001. doi:10.1007/3-540-44795-4_10

Markdown

[Eibl and Pfeiffer. "Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example." European Conference on Machine Learning, 2001.](https://mlanthology.org/ecmlpkdd/2001/eibl2001ecml-analysis/) doi:10.1007/3-540-44795-4_10

BibTeX

@inproceedings{eibl2001ecml-analysis,
  title     = {{Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example}},
  author    = {Eibl, Günther and Pfeiffer, Karl Peter},
  booktitle = {European Conference on Machine Learning},
  year      = {2001},
  pages     = {109-120},
  doi       = {10.1007/3-540-44795-4_10},
  url       = {https://mlanthology.org/ecmlpkdd/2001/eibl2001ecml-analysis/}
}