Pages that link to "Item:Q2581447"
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The following pages link to Shannon sampling. II: Connections to learning theory (Q2581447):
Displaying 50 items.
- Improved sampling and reconstruction in spline subspaces (Q287890) (← links)
- Whittaker-Kotel'nikov-Shannon approximation of \(\phi\)-sub-Gaussian random processes (Q302043) (← links)
- Multi-penalty regularization in learning theory (Q306697) (← links)
- Distributed parametric and nonparametric regression with on-line performance bounds computation (Q361001) (← links)
- Regularized least square regression with unbounded and dependent sampling (Q369717) (← links)
- Integral operator approach to learning theory with unbounded sampling (Q371679) (← links)
- Error bounds for \(l^p\)-norm multiple kernel learning with least square loss (Q448851) (← links)
- Consistency of regularized spectral clustering (Q533498) (← links)
- Frames, Riesz bases, and sampling expansions in Banach spaces via semi-inner products (Q544035) (← links)
- On the existence of optimal unions of subspaces for data modeling and clustering (Q544805) (← links)
- Error estimates from noise samples for iterative algorithm in shift-invariant signal spaces (Q610861) (← links)
- Least square regression with indefinite kernels and coefficient regularization (Q617706) (← links)
- Learning gradients via an early stopping gradient descent method (Q619042) (← links)
- On complex-valued 2D eikonals. IV: continuation past a caustic (Q627030) (← links)
- Unified approach to coefficient-based regularized regression (Q651513) (← links)
- Learning with varying insensitive loss (Q654259) (← links)
- Optimal rates for regularization of statistical inverse learning problems (Q667648) (← links)
- The convergence rate of a regularized ranking algorithm (Q692563) (← links)
- Variational splines and Paley-Wiener spaces on Combinatorial graphs (Q734052) (← links)
- Debiased magnitude-preserving ranking: learning rate and bias characterization (Q777111) (← links)
- Geometry on probability spaces (Q843724) (← links)
- Efficiency of classification methods based on empirical risk minimization (Q844356) (← links)
- Hermite learning with gradient data (Q848563) (← links)
- Regularized least square regression with dependent samples (Q849335) (← links)
- Sampling theory, oblique projections and a question by Smale and Zhou (Q849684) (← links)
- On regularization algorithms in learning theory (Q870339) (← links)
- Behavior of a functional in learning theory (Q934356) (← links)
- Simultaneous estimates for vector-valued Gabor frames of Hermite functions (Q960006) (← links)
- A note on application of integral operator in learning theory (Q1012558) (← links)
- Analysis of support vector machines regression (Q1022433) (← links)
- Optimal learning of bandlimited functions from localized sampling (Q1023396) (← links)
- General A-P iterative algorithm in shift-invariant spaces (Q1034278) (← links)
- Reconstructing signals with finite rate of innovation from noisy samples (Q1038748) (← links)
- High order Parzen windows and randomized sampling (Q1047130) (← links)
- Learning a function from noisy samples at a finite sparse set of points (Q1048968) (← links)
- Gradient learning in a classification setting by gradient descent (Q1048984) (← links)
- Approximation analysis of gradient descent algorithm for bipartite ranking (Q1760585) (← links)
- System identification using kernel-based regularization: new insights on stability and consistency issues (Q1797024) (← links)
- Generalization errors of Laplacian regularized least squares regression (Q1933952) (← links)
- Coefficient-based regression with non-identical unbounded sampling (Q2016624) (← links)
- Uniform bounds of aliasing and truncated errors in sampling series of functions from anisotropic Besov class (Q2016652) (← links)
- Random sampling and reconstruction of concentrated signals in a reproducing kernel space (Q2036501) (← links)
- An elementary analysis of ridge regression with random design (Q2080945) (← links)
- Divergence-free quasi-interpolation (Q2155816) (← links)
- Learning rate of distribution regression with dependent samples (Q2171946) (← links)
- Convergence analysis of Tikhonov regularization for non-linear statistical inverse problems (Q2192321) (← links)
- Approximation of Lyapunov functions from noisy data (Q2192453) (← links)
- Approximation properties of mixed sampling-Kantorovich operators (Q2211155) (← links)
- Distributed learning and distribution regression of coefficient regularization (Q2223571) (← links)
- Reproducing kernels and choices of associated feature spaces, in the form of \(L^2\)-spaces (Q2235966) (← links)
