ERM learning with unbounded sampling

DOI10.1007/s10114-012-9739-5zbMath1258.68072OpenAlexW2074107753MaRDI QIDQ1943018

Cheng Wang, Zheng-Chu Guo

Publication date: 14 March 2013

Published in: Acta Mathematica Sinica. English Series (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10114-012-9739-5

zbMATH Keywords

learning theorycovering numberleast square regressionERM

Mathematics Subject Classification ID

Computational learning theory (68Q32) Rate of convergence, degree of approximation (41A25)

Related Items (7)

Regularized least square regression with unbounded and dependent sampling ⋮ The learning rate of \(l_2\)-coefficient regularized classification with strong loss ⋮ Learning rates for the kernel regularized regression with a differentiable strongly convex loss ⋮ Support vector machines regression with unbounded sampling ⋮ Analysis of Regression Algorithms with Unbounded Sampling ⋮ Error bounds of the invariant statistics in machine learning of ergodic Itô diffusions ⋮ Half supervised coefficient regularization for regression learning with unbounded sampling

Cites Work

• Model selection for regularized least-squares algorithm in learning theory
• Multi-kernel regularized classifiers
• The covering number in learning theory
• Optimal rates for the regularized least-squares algorithm
• Learning with sample dependent hypothesis spaces
• Learning rates of least-square regularized regression
• Learning theory estimates via integral operators and their approximations
• On the mathematical foundations of learning
• Probability Inequalities for the Sum of Independent Random Variables
• Learning Theory
• Capacity of reproducing kernel spaces in learning theory
• ESTIMATING THE APPROXIMATION ERROR IN LEARNING THEORY
• Leave-One-Out Bounds for Kernel Methods

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