This work proposes a robust regression framework with nonconvex loss function. Two regression formulations are presented based on the Laplace kernel-induced ...
we propose a new robust regression framework based on Laplace kernel– induced loss function, and we present two regression formulations with. LK-loss.
Nov 1, 2017 · Abstract. This work proposes a robust regression framework with nonconvex loss function. Two regression formulations are presented based on ...
This letter proposes a robust regression framework with nonconvex loss function. Two regression formulations are presented based on the Laplace ...
This work proposes a robust regression framework with nonconvex loss function. Two regression formulations are presented based on the Laplace kernel-induced ...
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A Robust Regression Framework with Laplace Kernel-Induced Loss ...
colab.ws › articles › neco_a_01002
This work proposes a robust regression framework with nonconvex loss function. Two regression formulations are presented based on the Laplace kernel-induced ...
Mar 5, 2020 · This work proposes a robust loss function based on expectile penalty (named as rescaled expectile loss, RE-loss), which includes and ...
A Robust Regression Framework with Laplace Kernel-Induced Loss · Liming Yang, Zhuo Ren, Yidan Wang, Hongwei Dong. Neural Computation (2017) 29 (11): 3014–3039 ...
2017. TLDR. A robust regression framework with nonconvex loss function based on the Laplace kernel-induced loss (LK-loss) and a continuous optimization method ...
A robust regression framework with laplace kernel-induced loss. L Yang, Z Ren, Y Wang, H Dong. Neural computation 29 (11), 3014-3039, 2017. 23, 2017. Robust ...