Covering numbers of Gaussian reproducing kernel Hilbert spaces

DOI10.1016/j.jco.2011.01.005zbMath1221.68195OpenAlexW2114856622MaRDI QIDQ555034

Thomas Kühn

Publication date: 22 July 2011

Published in: Journal of Complexity (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jco.2011.01.005

zbMATH Keywords

learning theorycovering numberssmall deviationsGaussian RKHSsmooth Gaussian processes

Mathematics Subject Classification ID

Computational learning theory (68Q32) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (13)

Modal additive models with data-driven structure identification ⋮ On the equivalence of probability spaces ⋮ \( L_2\)-small ball asymptotics for Gaussian random functions: a survey ⋮ A close look at the entropy numbers of the unit ball of the reproducing Hilbert space of isotropic positive definite kernels ⋮ Approximations of semicontinuous functions with applications to stochastic optimization and statistical estimation ⋮ Learning Rates for Classification with Gaussian Kernels ⋮ A closer look at covering number bounds for Gaussian kernels ⋮ Small ball probabilities for smooth Gaussian fields and tensor products of compact operators ⋮ Balancing principle in supervised learning for a general regularization scheme ⋮ Variance-based regularization with convex objectives ⋮ Small deviations for a family of smooth Gaussian processes ⋮ Adaptive learning rates for support vector machines working on data with low intrinsic dimension ⋮ Sharp estimates for the covering numbers of the Weierstrass fractal kernel

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