Efficient Margin Maximizing with Boosting
Abstract
AdaBoost produces a linear combination of base hypotheses and predicts with the sign of this linear combination. The linear combination may be viewed as a hyperplane in feature space where the base hypotheses form the features. It has been observed that the generalization error of the algorithm continues to improve even after all examples are on the correct side of the current hyperplane. The improvement is attributed to the experimental observation that the distances (margins) of the examples to the separating hyperplane are increasing even after all examples are on the correct side.
Cite
Text
Rätsch and Warmuth. "Efficient Margin Maximizing with Boosting." Journal of Machine Learning Research, 2005.Markdown
[Rätsch and Warmuth. "Efficient Margin Maximizing with Boosting." Journal of Machine Learning Research, 2005.](https://mlanthology.org/jmlr/2005/ratsch2005jmlr-efficient/)BibTeX
@article{ratsch2005jmlr-efficient,
title = {{Efficient Margin Maximizing with Boosting}},
author = {Rätsch, Gunnar and Warmuth, Manfred K.},
journal = {Journal of Machine Learning Research},
year = {2005},
pages = {2131-2152},
volume = {6},
url = {https://mlanthology.org/jmlr/2005/ratsch2005jmlr-efficient/}
}