Learning with varying insensitive loss

DOI10.1016/j.aml.2011.06.007zbMath1228.62052OpenAlexW2016110516MaRDI QIDQ654259

Dao-Hong Xiang, Ting Hu, Ding-Xuan Zhou

Publication date: 28 December 2011

Published in: Applied Mathematics Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.aml.2011.06.007

zbMATH Keywords

approximationregressionreproducing kernel Hilbert spacesupport vector machine

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Learning and adaptive systems in artificial intelligence (68T05) Applications of functional analysis in probability theory and statistics (46N30)

Related Items (9)

ERM scheme for quantile regression ⋮ Approximation analysis of learning algorithms for support vector regression and quantile regression ⋮ Online learning for quantile regression and support vector regression ⋮ Learning with Convex Loss and Indefinite Kernels ⋮ Support vector machines regression with unbounded sampling ⋮ Calibration of \(\epsilon\)-insensitive loss in support vector machines regression ⋮ Learning rates of regression with q-norm loss and threshold ⋮ Unnamed Item ⋮ Comparison theorems on large-margin learning

Cites Work

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• Estimating conditional quantiles with the help of the pinball loss
• Analysis of support vector machines regression
• Learning rates of least-square regularized regression
• Shannon sampling. II: Connections to learning theory
• Support Vector Machines
• ONLINE LEARNING WITH MARKOV SAMPLING
• On Complexity Issues of Online Learning Algorithms

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