MATEC Web Conf.
Volume 173, 20182018 International Conference on Smart Materials, Intelligent Manufacturing and Automation (SMIMA 2018)
|Number of page(s)||8|
|Section||Digital Signal and Image Processing|
|Published online||19 June 2018|
Quantile Regression Learning with Coefficient Dependent lq-Regularizer
School of Mathematical Sciences, University of Jinan, People’s Republic of China
2 Caoxian No.1 Senior High School, People’s Republic of China
* Corresponding author: email@example.com
In this paper, We focus on conditional quantile regression learning algorithms based on the pinball loss and lq-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.
© The Authors, published by EDP Sciences, 2018
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