Online Classification on a Budget
Abstract
Online algorithms for classification often require vast amounts of mem- ory and computation time when employed in conjunction with kernel functions. In this paper we describe and analyze a simple approach for an on-the-fly reduction of the number of past examples used for prediction. Experiments performed with real datasets show that using the proposed algorithmic approach with a single epoch is competitive with the sup- port vector machine (SVM) although the latter, being a batch algorithm, accesses each training example multiple times.
Cite
Text
Crammer et al. "Online Classification on a Budget." Neural Information Processing Systems, 2003.Markdown
[Crammer et al. "Online Classification on a Budget." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/crammer2003neurips-online/)BibTeX
@inproceedings{crammer2003neurips-online,
title = {{Online Classification on a Budget}},
author = {Crammer, Koby and Kandola, Jaz and Singer, Yoram},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {225-232},
url = {https://mlanthology.org/neurips/2003/crammer2003neurips-online/}
}