Boosting on Manifolds: Adaptive Regularization of Base Classifiers
Abstract
In this paper we propose to combine two powerful ideas, boosting and manifold learning. On the one hand, we improve ADABOOST by incor- porating knowledge on the structure of the data into base classifier design and selection. On the other hand, we use ADABOOST's efficient learn- ing mechanism to significantly improve supervised and semi-supervised algorithms proposed in the context of manifold learning. Beside the spe- cific manifold-based penalization, the resulting algorithm also accommo- dates the boosting of a large family of regularized learning algorithms.
Cite
Text
Wang and Kégl. "Boosting on Manifolds: Adaptive Regularization of Base Classifiers." Neural Information Processing Systems, 2004.Markdown
[Wang and Kégl. "Boosting on Manifolds: Adaptive Regularization of Base Classifiers." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/wang2004neurips-boosting/)BibTeX
@inproceedings{wang2004neurips-boosting,
title = {{Boosting on Manifolds: Adaptive Regularization of Base Classifiers}},
author = {Wang, Ligen and Kégl, Balázs},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {665-672},
url = {https://mlanthology.org/neurips/2004/wang2004neurips-boosting/}
}