TY - JOUR
T1 - Online learning for quantile regression and support vector regression
AU - Hu, Ting
AU - Xiang, Dao-Hong
AU - Zhou, Ding-Xuan
PY - 2012/12
Y1 - 2012/12
N2 - 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.
AB - 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.
KW - Error analysis
KW - Insensitive pinball loss
KW - Online learning
KW - Quantile regression
KW - Reproducing kernel Hilbert space
KW - Support vector regression
UR - http://www.scopus.com/inward/record.url?scp=84865553052&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84865553052&origin=recordpage
U2 - 10.1016/j.jspi.2012.06.010
DO - 10.1016/j.jspi.2012.06.010
M3 - RGC 21 - Publication in refereed journal
SN - 0378-3758
VL - 142
SP - 3107
EP - 3122
JO - Journal of Statistical Planning and Inference
JF - Journal of Statistical Planning and Inference
IS - 12
ER -