Least-squares regularized regression with dependent samples andq-penalty

DOI10.1080/00036811.2011.559465zbMath1271.68203OpenAlexW1985951614WikidataQ58179031 ScholiaQ58179031MaRDI QIDQ2903163

Yun-Long Feng

Publication date: 23 August 2012

Published in: Applicable Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00036811.2011.559465

zbMATH Keywords

dependent samplesreproducing kernel Hilbert space\(q\)-penaltyleast-squares regularized regression

Mathematics Subject Classification ID

General nonlinear regression (62J02) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (5)

Fast learning from \(\alpha\)-mixing observations ⋮ Learning Theory Estimates with Observations from General Stationary Stochastic Processes ⋮ Unified approach to coefficient-based regularized regression ⋮ Constructive analysis for least squares regression with generalized \(K\)-norm regularization ⋮ Constructive analysis for coefficient regularization regression algorithms

Cites Work

• Model selection for regularized least-squares algorithm in learning theory
• Regularization in kernel learning
• Regularized least square regression with dependent samples
• Rates of convergence for empirical processes of stationary mixing sequences
• On consistency in nonparametric estimation under mixing conditions.
• Nonparametric time series prediction through adaptive model selection
• The covering number in learning theory
• 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
• Learning Theory
• Minimum complexity regression estimation with weakly dependent observations
• ESTIMATING THE APPROXIMATION ERROR IN LEARNING THEORY
• Leave-One-Out Bounds for Kernel Methods

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