Pages that link to "Item:Q5356934"
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The following pages link to Learning rates for regularized least squares ranking algorithm (Q5356934):
Displaying 33 items.
- Learning rate of support vector machine for ranking (Q468458) (← links)
- The convergence rate of a regularized ranking algorithm (Q692563) (← links)
- Debiased magnitude-preserving ranking: learning rate and bias characterization (Q777111) (← links)
- On kernel methods for covariates that are rankings (Q1657968) (← links)
- A linear functional strategy for regularized ranking (Q1669294) (← links)
- Convergence analysis of an empirical eigenfunction-based ranking algorithm with truncated sparsity (Q1722329) (← links)
- Bounding the difference between RankRC and RankSVM and application to multi-level rare class kernel ranking (Q1741234) (← links)
- Approximation analysis of gradient descent algorithm for bipartite ranking (Q1760585) (← links)
- Analysis of convergence performance of neural networks ranking algorithm (Q1942699) (← links)
- Robust pairwise learning with Huber loss (Q1979426) (← links)
- Convergence of online pairwise regression learning with quadratic loss (Q2191834) (← links)
- Quantitative convergence analysis of kernel based large-margin unified machines (Q2191836) (← links)
- Kernel gradient descent algorithm for information theoretic learning (Q2223567) (← links)
- Calibration and regret bounds for order-preserving surrogate losses in learning to rank (Q2251435) (← links)
- Efficient regularized least-squares algorithms for conditional ranking on relational data (Q2251443) (← links)
- On empirical eigenfunction-based ranking with \(\ell^1\) norm regularization (Q2256621) (← links)
- Learning rate of magnitude-preserving regularization ranking with dependent samples (Q2800842) (← links)
- Bias corrected regularization kernel method in ranking (Q4615656) (← links)
- Regularized Nyström subsampling in regression and ranking problems under general smoothness assumptions (Q4968723) (← links)
- Comparison theorems on large-margin learning (Q5022946) (← links)
- Learning theory of minimum error entropy under weak moment conditions (Q5037873) (← links)
- Distributed spectral pairwise ranking algorithms (Q5060714) (← links)
- On the K-functional in learning theory (Q5107666) (← links)
- Online regularized pairwise learning with least squares loss (Q5220066) (← links)
- Convergence analysis of distributed multi-penalty regularized pairwise learning (Q5220068) (← links)
- Semi-supervised learning with summary statistics (Q5236748) (← links)
- Distributed learning with indefinite kernels (Q5236752) (← links)
- Optimal learning with Gaussians and correntropy loss (Q5856264) (← links)
- On the convergence rate and some applications of regularized ranking algorithms (Q5963450) (← links)
- Error analysis of kernel regularized pairwise learning with a strongly convex loss (Q6112862) (← links)
- Optimality of regularized least squares ranking with imperfect kernels (Q6125450) (← links)
- Moduli of smoothness, \(K\)-functionals and Jackson-type inequalities associated with Kernel function approximation in learning theory (Q6587592) (← links)
- Analysis of regularized least squares ranking with centered reproducing kernel (Q6591691) (← links)
