A note on application of integral operator in learning theory
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Publication:1012558
DOI10.1016/j.acha.2008.10.002zbMath1165.68059OpenAlexW2055872486MaRDI QIDQ1012558
Publication date: 21 April 2009
Published in: Applied and Computational Harmonic Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.acha.2008.10.002
Related Items (22)
Coefficient regularized regression with non-iid sampling ⋮ Error analysis on Hérmite learning with gradient data ⋮ Distributed regression learning with coefficient regularization ⋮ Divergence-free quasi-interpolation ⋮ Distributed learning with partial coefficients regularization ⋮ Regularized least square regression with unbounded and dependent sampling ⋮ Integral operator approach to learning theory with unbounded sampling ⋮ Least square regression with indefinite kernels and coefficient regularization ⋮ Spectral algorithms for learning with dependent observations ⋮ Regression learning with non-identically and non-independently sampling ⋮ Learning rates of multitask kernel methods ⋮ Concentration estimates for learning with unbounded sampling ⋮ Consistency analysis of spectral regularization algorithms ⋮ Error bounds for \(l^p\)-norm multiple kernel learning with least square loss ⋮ Fast learning from \(\alpha\)-mixing observations ⋮ Learning Theory Estimates with Observations from General Stationary Stochastic Processes ⋮ Least-square regularized regression with non-iid sampling ⋮ Application of integral operator for vector-valued regression learning ⋮ Estimations of singular functions of kernel cross-covariance operators ⋮ Debiased magnitude-preserving ranking: learning rate and bias characterization ⋮ Unnamed Item ⋮ Unnamed Item
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