Indefinite kernel network with \(l^q\)-norm regularization

DOI10.1155/2016/6516258zbMath1417.62093OpenAlexW2324680782WikidataQ59123613 ScholiaQ59123613MaRDI QIDQ1723692

Zhongfeng Qu, Hongwei Sun

Publication date: 19 February 2019

Published in: Discrete Dynamics in Nature and Society (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2016/6516258

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20)

Cites Work

• Learning by nonsymmetric kernels with data dependent spaces and \(\ell^1\)-regularizer
• Concentration estimates for learning with \(\ell ^{1}\)-regularizer and data dependent hypothesis spaces
• Least square regression with indefinite kernels and coefficient regularization
• Regularized least square regression with dependent samples
• Learning theory estimates for coefficient-based regularized regression
• Learning with sample dependent hypothesis spaces
• Learning rates of regularized regression for exponentially strongly mixing sequence
• Learning rates of least-square regularized regression
• Learning theory estimates via integral operators and their approximations
• INDEFINITE KERNEL NETWORK WITH DEPENDENT SAMPLING
• Learning Theory
• 10.1162/153244302760200704
• Leave-One-Out Bounds for Kernel Methods
• Theory of Reproducing Kernels

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