The following pages link to (Q3093228):
Displaying 35 items.
- Kernel-based conditional canonical correlation analysis via modified Tikhonov regularization (Q326761) (← links)
- The learning rate of \(l_2\)-coefficient regularized classification with strong loss (Q383667) (← links)
- On qualitative robustness of support vector machines (Q538179) (← links)
- Convergence rate of kernel canonical correlation analysis (Q659987) (← links)
- Consistent learning by composite proximal thresholding (Q681492) (← links)
- Regularization in kernel learning (Q847647) (← links)
- Hermite learning with gradient data (Q848563) (← links)
- Ideal regularization for learning kernels from labels (Q889267) (← links)
- Derivative reproducing properties for kernel methods in learning theory (Q939547) (← links)
- Learning from dependent observations (Q958916) (← links)
- The convergence rate for a \(K\)-functional in learning theory (Q962475) (← links)
- An algebraic characterization of the optimum of regularized kernel methods (Q1009329) (← links)
- Consistency of support vector machines for forecasting the evolution of an unknown ergodic dynamical system from observations with unknown noise (Q1020982) (← links)
- Gradient learning in a classification setting by gradient descent (Q1048984) (← links)
- Robustness of reweighted least squares kernel based regression (Q1049548) (← links)
- On asymptotic properties of hyperparameter estimators for kernel-based regularization methods (Q1797138) (← links)
- Conditional quantiles with varying Gaussians (Q1955538) (← links)
- Asymptotic linear expansion of regularized M-estimators (Q2075454) (← links)
- Robustness by reweighting for kernel estimators: an overview (Q2075710) (← links)
- Learning from non-random data in Hilbert spaces: an optimal recovery perspective (Q2143167) (← links)
- A new randomized Kaczmarz based kernel canonical correlation analysis algorithm with applications to information retrieval (Q2179300) (← links)
- Learning rates for the kernel regularized regression with a differentiable strongly convex loss (Q2191832) (← links)
- Consistency and robustness of kernel-based regression in convex risk minimization (Q2469652) (← links)
- Convex optimization in sums of Banach spaces (Q2667036) (← links)
- Supervised Learning by Support Vector Machines (Q2789826) (← links)
- Regularized learning schemes in feature Banach spaces (Q4594821) (← links)
- (Q4998897) (← links)
- Error analysis of the kernel regularized regression based on refined convex losses and RKBSs (Q5022936) (← links)
- On the K-functional in learning theory (Q5107666) (← links)
- Coefficient-based regularization network with variance loss for error (Q5150110) (← links)
- Performance analysis of the LapRSSLG algorithm in learning theory (Q5220067) (← links)
- Generalized support vector regression: Duality and tensor-kernel representation (Q5220070) (← links)
- Assistive Optimal Control-on-Request with Application in Standing Balance Therapy and Reinforcement (Q5223139) (← links)
- Constrained ERM Learning of Canonical Correlation Analysis: A Least Squares Perspective (Q5380852) (← links)
- Online Classification with Varying Gaussians (Q5851123) (← links)
