TY - GEN
T1 - Online classification on a budget
AU - Crammer, Koby
AU - Kandola, Jaz
AU - Singer, Yoram
PY - 2004
Y1 - 2004
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84899003168&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84899003168&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84899003168
SN - 0262201526
SN - 9780262201520
T3 - Advances in Neural Information Processing Systems
BT - Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003
PB - Neural information processing systems foundation
T2 - 17th Annual Conference on Neural Information Processing Systems, NIPS 2003
Y2 - 8 December 2003 through 13 December 2003
ER -