Approximation analysis of learning algorithms for support vector regression and quantile regression

DOI10.1155/2012/902139zbMath1235.68206OpenAlexW1974709327WikidataQ58906907 ScholiaQ58906907MaRDI QIDQ411126

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

Publication date: 4 April 2012

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

Full work available at URL: https://doi.org/10.1155/2012/902139

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (13)

Asymmetric \(\nu\)-tube support vector regression ⋮ Statistical consistency of coefficient-based conditional quantile regression ⋮ ERM scheme for quantile regression ⋮ Quantitative convergence analysis of kernel based large-margin unified machines ⋮ Error analysis of classification learning algorithms based on LUMs loss ⋮ Support vector machines regression with unbounded sampling ⋮ A simpler approach to coefficient regularized support vector machines regression ⋮ Calibration of \(\epsilon\)-insensitive loss in support vector machines regression ⋮ Coefficient-based regularization network with variance loss for error ⋮ Perturbation of convex risk minimization and its application in differential private learning algorithms ⋮ Moving quantile regression ⋮ Learning rates of regression with q-norm loss and threshold ⋮ Unnamed Item

Cites Work

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• Online learning for quantile regression and support vector regression
• Estimating conditional quantiles with the help of the pinball loss
• Learning with varying insensitive loss
• Fast rates for support vector machines using Gaussian kernels
• Analysis of support vector machines regression
• The covering number in learning theory
• Conditional quantiles with varying Gaussians
• Convergence analysis of online algorithms
• Learning rates of least-square regularized regression
• Learning theory estimates via integral operators and their approximations
• ONLINE REGRESSION WITH VARYING GAUSSIANS AND NON-IDENTICAL DISTRIBUTIONS
• Learning Theory
• Capacity of reproducing kernel spaces in learning theory
• ONLINE LEARNING WITH MARKOV SAMPLING
• On Complexity Issues of Online Learning Algorithms
• Theory of Reproducing Kernels

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