Online learning for quantile regression and support vector regression

We consider for quantile regression and support vector regression a kernel-based online learning algorithm associated with a sequence of insensitive pinball loss functions. Our error analysis and derived learning rates show quantitatively that the statistical performance of the learning algorithm may vary with the quantile parameter In our analysis we overcome the technical difficulty caused by the varying insensitive parameter introduced with a motivation of sparsity. © 2012 Elsevier B.V.