Pages that link to "Item:Q2642918"

The following pages link to Learning theory estimates via integral operators and their approximations (Q2642918):

Displaying 50 items.

• Reproducing kernel Hilbert spaces associated with analytic translation-invariant Mercer kernels (Q939089) (← links)
• Derivative reproducing properties for kernel methods in learning theory (Q939547) (← links)
• Parzen windows for multi-class classification (Q958247) (← links)
• Learning and approximation by Gaussians on Riemannian manifolds (Q960002) (← links)
• Multivariate Bernstein-Durrmeyer operators with arbitrary weight functions (Q968962) (← links)
• Moving least-square method in learning theory (Q968963) (← links)
• A new kernel-based approach for linear system identification (Q985267) (← links)
• Image and video colorization using vector-valued reproducing kernel Hilbert spaces (Q993570) (← links)
• Estimates of the approximation error using Rademacher complexity: Learning vector-valued functions (Q1008456) (← links)
• A note on application of integral operator in learning theory (Q1012558) (← links)
• Analysis of support vector machines regression (Q1022433) (← links)
• Elastic-net regularization in learning theory (Q1023403) (← links)
• High order Parzen windows and randomized sampling (Q1047130) (← links)
• Learning theory and approximation. Abstracts from the workshop held June 29 -- July 5, 2008. (Q1047813) (← links)
• Gradient learning in a classification setting by gradient descent (Q1048984) (← links)
• Thresholding projection estimators in functional linear models (Q1049544) (← links)
• Error analysis on Hérmite learning with gradient data (Q1624095) (← links)
• On the stability of reproducing kernel Hilbert spaces of discrete-time impulse responses (Q1626948) (← links)
• Nonparametric regression using needlet kernels for spherical data (Q1633627) (← links)
• Optimal learning rates for kernel partial least squares (Q1645280) (← links)
• Kernel methods for the approximation of some key quantities of nonlinear systems (Q1654461) (← links)
• A linear functional strategy for regularized ranking (Q1669294) (← links)
• Error analysis for coefficient-based regularized regression in additive models (Q1698243) (← links)
• Convergence analysis of an empirical eigenfunction-based ranking algorithm with truncated sparsity (Q1722329) (← links)
• Indefinite kernel network with \(l^q\)-norm regularization (Q1723692) (← links)
• Analysis of approximation by linear operators on variable \(L_\rho^{p(\cdot)}\) spaces and applications in learning theory (Q1724144) (← links)
• Constructive analysis for least squares regression with generalized \(K\)-norm regularization (Q1724159) (← links)
• Distributed kernel-based gradient descent algorithms (Q1745365) (← links)
• Approximation analysis of gradient descent algorithm for bipartite ranking (Q1760585) (← links)
• Coefficient-based \(l^q\)-regularized regression with indefinite kernels and unbounded sampling (Q1784975) (← links)
• Analysis of regularized least squares for functional linear regression model (Q1791683) (← links)
• System identification using kernel-based regularization: new insights on stability and consistency issues (Q1797024) (← links)
• Continuum versus discrete networks, graph Laplacians, and reproducing kernel Hilbert spaces (Q1799154) (← links)
• Learning rates for least square regressions with coefficient regularization (Q1928153) (← links)
• An extension of Mercer's theory to \(L^p\) (Q1928541) (← links)
• ERM learning with unbounded sampling (Q1943018) (← links)
• The regularized least squares algorithm and the problem of learning halfspaces (Q1944907) (← links)
• Concentration estimates for learning with unbounded sampling (Q1946480) (← links)
• Optimal regression rates for SVMs using Gaussian kernels (Q1951100) (← links)
• Adaptive kernel methods using the balancing principle (Q1959089) (← links)
• Kernel conjugate gradient methods with random projections (Q1979923) (← links)
• Generalization ability of online pairwise support vector machine (Q1996328) (← links)
• Adaptive estimation for nonlinear systems using reproducing kernel Hilbert spaces (Q2000503) (← links)
• Coefficient-based regression with non-identical unbounded sampling (Q2016624) (← links)
• Asymptotic expansion for neural network operators of the Kantorovich type and high order of approximation (Q2023320) (← links)
• Estimations of singular functions of kernel cross-covariance operators (Q2029807) (← links)
• Learning sparse conditional distribution: an efficient kernel-based approach (Q2044348) (← links)
• The \(\mathrm{r}\)-\(\mathrm{d}\) class predictions in linear mixed models (Q2048216) (← links)
• Estimation of the number of components of nonparametric multivariate finite mixture models (Q2054486) (← links)
• Theory of deep convolutional neural networks. II: Spherical analysis (Q2057723) (← links)