Approximation Analysis of Learning Algorithms for Support Vector Regression and Quantile Regression
We study learning algorithms generated by regularization schemes in reproducing kernel Hilbert spaces associated with an ϵ-insensitive pinball loss. This loss function is motivated by the ϵ-insensitive loss for support vector regression and the pinball loss for quantile regression. Approximation ana...
Saved in:
| Main Authors: | Dao-Hong Xiang, Ting Hu, Ding-Xuan Zhou |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
|
| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2012/902139 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
ERM Scheme for Quantile Regression
by: Dao-Hong Xiang
Published: (2013-01-01) -
Identification of Wiener Model with Internal Noise Using a Cubic Spline Approximation-Bayesian Composite Quantile Regression Algorithm
by: Tianhong Pan, et al.
Published: (2020-01-01) -
QUANTILE REGRESSION MODEL ON RAINFALL IN MAKASSAR 2019
by: Wahidah Sanusi, et al.
Published: (2023-04-01) -
COMPARISON BETWEEN BAYESIAN QUANTILE REGRESSION AND BAYESIAN LASSO QUANTILE REGRESSION FOR MODELING POVERTY LINE WITH PRESENCE OF HETEROSCEDASTICITY IN WEST SUMATRA
by: Lilis Harianti Hasibuan, et al.
Published: (2025-07-01) -
Blind equalization algorithm based on complex support vector regression
by: Ling YANG, et al.
Published: (2019-10-01)