The Huller: A Simple and Efficient Online SVM
Abstract
We propose a novel online kernel classifier algorithm that converges to the Hard Margin SVM solution. The same update rule is used to both add and remove support vectors from the current classifier. Experiments suggest that this algorithm matches the SVM accuracies after a single pass over the training examples. This algorithm is attractive when one seeks a competitive classifier with large datasets and limited computing resources.
Cite
Text
Bordes and Bottou. "The Huller: A Simple and Efficient Online SVM." European Conference on Machine Learning, 2005. doi:10.1007/11564096_48Markdown
[Bordes and Bottou. "The Huller: A Simple and Efficient Online SVM." European Conference on Machine Learning, 2005.](https://mlanthology.org/ecmlpkdd/2005/bordes2005ecml-huller/) doi:10.1007/11564096_48BibTeX
@inproceedings{bordes2005ecml-huller,
title = {{The Huller: A Simple and Efficient Online SVM}},
author = {Bordes, Antoine and Bottou, Léon},
booktitle = {European Conference on Machine Learning},
year = {2005},
pages = {505-512},
doi = {10.1007/11564096_48},
url = {https://mlanthology.org/ecmlpkdd/2005/bordes2005ecml-huller/}
}