Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume 14, Issue 6, Pages 1449-1459Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2003.820828
Keywords
reduced support vector machine; support vector machine
Ask authors/readers for more resources
Recently the reduced support vector machine (RSVM) was proposed as an alternate of the standard SVM. Motivated by resolving the difficulty on handling large data sets using SVM with nonlinear kernels, it preselects a subset of data as support vectors and solves a smaller optimization problem. However, several issues of its practical use have not been fully discussed yet. For example, we do not know if it possesses comparable generalization ability as the standard SVM. In addition, we would like to see for how large problems RSVM outperforms SVM on training time. In this paper we show that the RSVM formulation is already in a form of linear SVM and discuss four RSVM implementations. Experiments indicate that in general the test accuracy of RSVM are a little lower than that of the standard SVM. In addition, for problems with up to tens of thousands of data, if the percentage of support vectors is not high, existing implementations for SVM is quite competitive on the training time. Thus, from this empirical study, RSVM will be mainly useful for either larger problems or those with many support vectors. Experiments in this paper also serve as comparisons of. 1) different implementations for linear SVM and 2) standard SVM using linear and quadratic cost functions.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available