Multiplicative updates For Non-Negative Kernel SVM
Machine Learning
2009-02-25 v1
Abstract
We present multiplicative updates for solving hard and soft margin support vector machines (SVM) with non-negative kernels. They follow as a natural extension of the updates for non-negative matrix factorization. No additional param- eter setting, such as choosing learning, rate is required. Ex- periments demonstrate rapid convergence to good classifiers. We analyze the rates of asymptotic convergence of the up- dates and establish tight bounds. We test the performance on several datasets using various non-negative kernels and report equivalent generalization errors to that of a standard SVM.
Cite
@article{arxiv.0902.4228,
title = {Multiplicative updates For Non-Negative Kernel SVM},
author = {Vamsi K. Potluru and Sergey M. Plis and Morten Morup and Vince D. Calhoun and Terran Lane},
journal= {arXiv preprint arXiv:0902.4228},
year = {2009}
}
Comments
4 pages, 1 figure, 1 table