Pages that link to "Item:Q2642918"

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

Displaying 50 items.

• Statistical consistency of coefficient-based conditional quantile regression (Q290691) (← links)
• Multi-penalty regularization in learning theory (Q306697) (← links)
• Nonparametric stochastic approximation with large step-sizes (Q309706) (← links)
• Kernel-based conditional canonical correlation analysis via modified Tikhonov regularization (Q326761) (← links)
• Learning theory and approximation. Abstracts from the workshop held June 24--30, 2012. (Q343343) (← 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)
• Sharp learning rates of coefficient-based \(l^q\)-regularized regression with indefinite kernels (Q370936) (← links)
• Integral operator approach to learning theory with unbounded sampling (Q371679) (← links)
• Learning with coefficient-based regularization and \(\ell^1\)-penalty (Q380980) (← links)
• The learning rate of \(l_2\)-coefficient regularized classification with strong loss (Q383667) (← links)
• Least squares regression with \(l_1\)-regularizer in sum space (Q390496) (← links)
• Random design analysis of ridge regression (Q404306) (← links)
• Approximation analysis of learning algorithms for support vector regression and quantile regression (Q411126) (← links)
• An empirical feature-based learning algorithm producing sparse approximations (Q413648) (← links)
• On the regularized Laplacian eigenmaps (Q419255) (← links)
• Mercer's theorem on general domains: on the interaction between measures, kernels, and RKHSs (Q431161) (← links)
• Consistency analysis of spectral regularization algorithms (Q437553) (← links)
• Estimation of convergence rate for multi-regression learning algorithm (Q439762) (← links)
• Error bounds for \(l^p\)-norm multiple kernel learning with least square loss (Q448851) (← links)
• Kernel methods in system identification, machine learning and function estimation: a survey (Q462325) (← links)
• Learning rate of support vector machine for ranking (Q468458) (← links)
• Constructive analysis for coefficient regularization regression algorithms (Q491841) (← links)
• Perturbation of convex risk minimization and its application in differential private learning algorithms (Q504548) (← links)
• Consistency of regularized spectral clustering (Q533498) (← links)
• Learning from non-identical sampling for classification (Q541601) (← links)
• Concentration estimates for learning with \(\ell ^{1}\)-regularizer and data dependent hypothesis spaces (Q550498) (← links)
• Optimal learning rates for least squares regularized regression with unbounded sampling (Q617656) (← 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)
• Prediction error identification of linear systems: a nonparametric Gaussian regression approach (Q627072) (← links)
• Learning theory viewpoint of approximation by positive linear operators (Q630715) (← links)
• Semi-supervised learning with the help of Parzen windows (Q640960) (← links)
• New robust unsupervised support vector machines (Q646730) (← links)
• Classification with non-i.i.d. sampling (Q652859) (← links)
• Convergence rate of kernel canonical correlation analysis (Q659987) (← links)
• Optimal rates for regularization of statistical inverse learning problems (Q667648) (← links)
• Statistical performance of optimal scoring in reproducing kernel Hilbert spaces (Q680400) (← links)
• The convergence rate of a regularized ranking algorithm (Q692563) (← links)
• Debiased magnitude-preserving ranking: learning rate and bias characterization (Q777111) (← links)
• Geometry on probability spaces (Q843724) (← links)
• Regularization in kernel learning (Q847647) (← links)
• Hermite learning with gradient data (Q848563) (← links)
• Regularized least square regression with dependent samples (Q849335) (← links)
• Multi-kernel regularized classifiers (Q870343) (← links)
• Learning theory approach to a system identification problem involving atomic norm (Q895425) (← links)
• 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)