Quantile Regression Learning with Coefficient Dependent l~q-Regularizer

摘要

In this paper, We focus on conditional quantile regression learning algorithms based on the pinball loss and l~q-regularizer with 1≤q≤2. Our main goal is to study the consistency of this kind of regularized quantile regression learning. With concentration inequality and operator decomposition techniques, we obtained satisfied error bounds and convergence rates.