Learning rates of regression with q-norm loss and threshold

DOI10.1142/S0219530516400030zbMath1394.62044arXiv1701.01956OpenAlexW2515063787MaRDI QIDQ2835987

Ting Hu, Yuan Yao

Publication date: 30 November 2016

Published in: Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1701.01956

zbMATH Keywords

quantile regressionreproducing kernel Hilbert spacesparsityinsensitive \(q\)-norm loss

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric robustness (62G35) Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

• Approximation analysis of learning algorithms for support vector regression and quantile regression
• Online learning for quantile regression and support vector regression
• Learning with varying insensitive loss
• INDEFINITE KERNEL NETWORK WITH DEPENDENT SAMPLING
• ESTIMATING THE APPROXIMATION ERROR IN LEARNING THEORY
• 10.1162/153244302760200713
• Regularization schemes for minimum error entropy principle
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