  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.
