One-Pass Boosting

Zafer Barutcuoglu, Phil Long, Rocco Servedio

NeurIPS 2007 pp. 73-80

/neurips/2007/barutcuoglu2007neurips-onepass/

Abstract

This paper studies boosting algorithms that make a single pass over a set of base classi(cid:2)ers. We (cid:2)rst analyze a one-pass algorithm in the setting of boosting with diverse base classi(cid:2)ers. Our guarantee is the same as the best proved for any boosting algo- rithm, but our one-pass algorithm is much faster than previous approaches. We next exhibit a random source of examples for which a (cid:147)picky(cid:148) variant of Ad- aBoost that skips poor base classi(cid:2)ers can outperform the standard AdaBoost al- gorithm, which uses every base classi(cid:2)er, by an exponential factor. Experiments with Reuters and synthetic data show that one-pass boosting can sub- stantially improve on the accuracy of Naive Bayes, and that picky boosting can sometimes lead to a further improvement in accuracy.

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Cite

Text

Barutcuoglu et al. "One-Pass Boosting." Neural Information Processing Systems, 2007.

Markdown

[Barutcuoglu et al. "One-Pass Boosting." Neural Information Processing Systems, 2007.](https://mlanthology.org/neurips/2007/barutcuoglu2007neurips-onepass/)

BibTeX

@inproceedings{barutcuoglu2007neurips-onepass,
  title     = {{One-Pass Boosting}},
  author    = {Barutcuoglu, Zafer and Long, Phil and Servedio, Rocco},
  booktitle = {Neural Information Processing Systems},
  year      = {2007},
  pages     = {73-80},
  url       = {https://mlanthology.org/neurips/2007/barutcuoglu2007neurips-onepass/}
}