@inproceedings{c5fdcb9f25454e7ea640a588e6694761,
title = "Online classification on a budget",
abstract = "Online algorithms for classification often require vast amounts of memory 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 support vector machine (SVM) although the latter, being a batch algorithm, accesses each training example multiple times.",
author = "Koby Crammer and Jaz Kandola and Yoram Singer",
year = "2004",
language = "English (US)",
isbn = "0262201526",
series = "Advances in Neural Information Processing Systems",
publisher = "Neural information processing systems foundation",
booktitle = "Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003",
note = "17th Annual Conference on Neural Information Processing Systems, NIPS 2003 ; Conference date: 08-12-2003 Through 13-12-2003",
}