A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf
.
NESVM: A Fast Gradient Method for Support Vector Machines
2010
2010 IEEE International Conference on Data Mining
Support vector machines (SVMs) are invaluable tools for many practical applications in artificial intelligence, e.g., classification and event recognition. However, popular SVM solvers are not sufficiently efficient for applications with a great deal of samples as well as a large number of features. In this paper, thus, we present NESVM, a fast gradient SVM solver that can optimize various SVM models, e.g., classical SVM, linear programming SVM and least square SVM. Compared against SVM-Perf
doi:10.1109/icdm.2010.135
dblp:conf/icdm/ZhouTW10
fatcat:czrtkottbnh6fjpr3ucjk2445e