Pages that link to "Item:Q2385535"
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The following pages link to Optimal rates for the regularized least-squares algorithm (Q2385535):
Displaying 50 items.
- Half supervised coefficient regularization for regression learning with unbounded sampling (Q2855757) (← links)
- Online regression with unbounded sampling (Q2885522) (← links)
- Optimal rate of the regularized regression learning algorithm (Q3008355) (← links)
- Rademacher Chaos Complexities for Learning the Kernel Problem (Q3057230) (← links)
- Least Square Regression with <i>l<sup>p</sup></i>-Coefficient Regularization (Q3067078) (← links)
- A NOTE ON STABILITY OF ERROR BOUNDS IN STATISTICAL LEARNING THEORY (Q3096969) (← links)
- Estimates of learning rates of regularized regression via polyline functions (Q3118875) (← links)
- LOCAL LEARNING ESTIMATES BY INTEGRAL OPERATORS (Q3161590) (← links)
- (Q3180560) (← links)
- Kernel regression, minimax rates and effective dimensionality: Beyond the regular case (Q3298576) (← links)
- The Random Feature Model for Input-Output Maps between Banach Spaces (Q3382802) (← links)
- Analysis of Regression Algorithms with Unbounded Sampling (Q3386411) (← links)
- Convergence Rates of Spectral Regularization Methods: A Comparison between Ill-Posed Inverse Problems and Statistical Kernel Learning (Q3386994) (← links)
- VECTOR VALUED REPRODUCING KERNEL HILBERT SPACES AND UNIVERSALITY (Q3406713) (← links)
- Spectral Algorithms for Supervised Learning (Q3510946) (← links)
- CROSS-VALIDATION BASED ADAPTATION FOR REGULARIZATION OPERATORS IN LEARNING THEORY (Q3560100) (← links)
- Quantum machine learning: a classical perspective (Q4556858) (← links)
- (Q4558172) (← links)
- (Q4558477) (← links)
- Gradient descent for robust kernel-based regression (Q4571003) (← links)
- Optimal rates for Lavrentiev regularization with adjoint source conditions (Q4600708) (← links)
- (Q4633060) (← links)
- (Q4637006) (← links)
- Optimal Rates for Multi-pass Stochastic Gradient Methods (Q4637012) (← links)
- Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression (Q4637017) (← links)
- (Q4637042) (← links)
- Kernel partial least squares for stationary data (Q4637047) (← links)
- On the Decay Rate of the Singular Values of Bivariate Functions (Q4637761) (← links)
- Nyström subsampling method for coefficient-based regularized regression (Q4968314) (← links)
- Regularized Nyström subsampling in regression and ranking problems under general smoothness assumptions (Q4968723) (← links)
- (Q4969055) (← links)
- (Q4969157) (← links)
- (Q4969211) (← links)
- Gradient-Based Kernel Dimension Reduction for Regression (Q4975356) (← links)
- (Q4998897) (← links)
- (Q4998979) (← links)
- (Q4999061) (← links)
- Multikernel Regression with Sparsity Constraint (Q4999353) (← links)
- Multiple Kernel Learningの学習理論 (Q5011460) (← links)
- On the Effectiveness of Richardson Extrapolation in Data Science (Q5018900) (← links)
- Distributed least squares prediction for functional linear regression* (Q5019925) (← links)
- Generalisation error in learning with random features and the hidden manifold model* (Q5020057) (← links)
- Error analysis of the kernel regularized regression based on refined convex losses and RKBSs (Q5022936) (← links)
- Comparison theorems on large-margin learning (Q5022946) (← links)
- Regularization: From Inverse Problems to Large-Scale Machine Learning (Q5028166) (← links)
- Optimal minimax rates against nonsmooth alternatives (Q5053108) (← links)
- (Q5053180) (← links)
- (Q5053236) (← links)
- (Q5054630) (← links)
- Learning curves of generic features maps for realistic datasets with a teacher-student model* (Q5055409) (← links)
