TY - GEN

T1 - On approximate solutions to support vector machines

AU - Cao, Dongwei

AU - Boley, Daniel

PY - 2006

Y1 - 2006

N2 - We propose to speed up the training process of support vector machines (SVM) by resorting to an approximate SVM, where a small number of representatives are extracted from the original training data set and used for training. Theoretical studies show that, in order for the approximate SVM to be similar to the exact SVM given by the original training data set, kernel k-means should be used to extract the representatives. As practical variations, we also propose two efficient implementations of the proposed algorithm, where approximations to kernel k-means are used. The proposed algorithms are compared against the standard training algorithm over real data sets.

AB - We propose to speed up the training process of support vector machines (SVM) by resorting to an approximate SVM, where a small number of representatives are extracted from the original training data set and used for training. Theoretical studies show that, in order for the approximate SVM to be similar to the exact SVM given by the original training data set, kernel k-means should be used to extract the representatives. As practical variations, we also propose two efficient implementations of the proposed algorithm, where approximations to kernel k-means are used. The proposed algorithms are compared against the standard training algorithm over real data sets.

UR - http://www.scopus.com/inward/record.url?scp=33745455332&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33745455332&partnerID=8YFLogxK

U2 - 10.1137/1.9781611972764.55

DO - 10.1137/1.9781611972764.55

M3 - Conference contribution

AN - SCOPUS:33745455332

SN - 089871611X

SN - 9780898716115

T3 - Proceedings of the Sixth SIAM International Conference on Data Mining

SP - 534

EP - 538

BT - Proceedings of the Sixth SIAM International Conference on Data Mining

PB - Society for Industrial and Applied Mathematics

T2 - Sixth SIAM International Conference on Data Mining

Y2 - 20 April 2006 through 22 April 2006

ER -