SERBoost: Semi-Supervised Boosting with Expectation Regularization
Abstract
The application of semi-supervised learning algorithms to large scale vision problems suffers from the bad scaling behavior of most methods. Based on the Expectation Regularization principle, we propose a novel semi-supervised boosting method, called SERBoost that can be applied to large scale vision problems. The complexity is mainly dominated by the base learners. The algorithm provides a margin regularizer for the boosting cost function and shows a principled way of utilizing prior knowledge. We demonstrate the performance of SERBoost on the Pascal VOC2006 set and compare it to other supervised and semi-supervised methods, where SERBoost shows improvements both in terms of classification accuracy and computational speed.
Cite
Text
Saffari et al. "SERBoost: Semi-Supervised Boosting with Expectation Regularization." European Conference on Computer Vision, 2008. doi:10.1007/978-3-540-88690-7_44Markdown
[Saffari et al. "SERBoost: Semi-Supervised Boosting with Expectation Regularization." European Conference on Computer Vision, 2008.](https://mlanthology.org/eccv/2008/saffari2008eccv-serboost/) doi:10.1007/978-3-540-88690-7_44BibTeX
@inproceedings{saffari2008eccv-serboost,
title = {{SERBoost: Semi-Supervised Boosting with Expectation Regularization}},
author = {Saffari, Amir and Grabner, Helmut and Bischof, Horst},
booktitle = {European Conference on Computer Vision},
year = {2008},
pages = {588-601},
doi = {10.1007/978-3-540-88690-7_44},
url = {https://mlanthology.org/eccv/2008/saffari2008eccv-serboost/}
}