Title: Exponential convergence of testing error for stochastic gradient methods,
Abstract: We consider binary classification problems with positive definite kernels and
square loss, and study the convergence rates of stochastic gradient methods. We
show that while the excess testing loss (squared loss) converges slowly to zero
as the number of observations (and thus iterations) goes to infinity, the
testing error (classification error) converges exponentially fast if low-noise
conditions are assumed.