Pages that link to "Item:Q3547665"
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The following pages link to Consistency of Support Vector Machines and Other Regularized Kernel Classifiers (Q3547665):
Displaying 50 items.
- Classification in general finite dimensional spaces with the \(k\)-nearest neighbor rule (Q292872) (← links)
- Robustness and generalization (Q420915) (← links)
- Statistical analysis of kernel-based least-squares density-ratio estimation (Q420923) (← links)
- Oracle properties of SCAD-penalized support vector machine (Q433741) (← links)
- Does modeling lead to more accurate classification? A study of relative efficiency in linear classification (Q476236) (← links)
- On qualitative robustness of support vector machines (Q538179) (← links)
- Generalization performance of least-square regularized regression algorithm with Markov chain samples (Q662073) (← links)
- Relaxing support vectors for classification (Q744716) (← links)
- Generalization performance of Lagrangian support vector machine based on Markov sampling (Q830752) (← links)
- A Fisher consistent multiclass loss function with variable margin on positive examples (Q887268) (← links)
- Learning from dependent observations (Q958916) (← links)
- Fast rates for support vector machines using Gaussian kernels (Q995417) (← links)
- Robust learning from bites for data mining (Q1020821) (← links)
- On surrogate loss functions and \(f\)-divergences (Q1020983) (← links)
- Demonstrating the stability of support vector machines for classification (Q1031290) (← links)
- Simulation-based classification; a model-order-reduction approach for structural health monitoring (Q1639582) (← links)
- A two-experiment approach to Wiener system identification (Q1797016) (← links)
- Support vector machines are universally consistent (Q1872633) (← links)
- Calibrated asymmetric surrogate losses (Q1950846) (← links)
- Learning sparse conditional distribution: an efficient kernel-based approach (Q2044348) (← links)
- Variational analysis of constrained M-estimators (Q2215758) (← links)
- Learning with mitigating random consistency from the accuracy measure (Q2217414) (← links)
- Kernel variable selection for multicategory support vector machines (Q2237819) (← links)
- Robustness of learning algorithms using hinge loss with outlier indicators (Q2292231) (← links)
- Local Rademacher complexities and oracle inequalities in risk minimization. (2004 IMS Medallion Lecture). (With discussions and rejoinder) (Q2373576) (← links)
- The new interpretation of support vector machines on statistical learning theory (Q2379242) (← links)
- On the consistency of multi-label learning (Q2446585) (← links)
- Consistency and robustness of kernel-based regression in convex risk minimization (Q2469652) (← links)
- On the rate of convergence for multi-category classification based on convex losses (Q2475308) (← links)
- A consistent information criterion for support vector machines in diverging model spaces (Q2810782) (← links)
- A NOTE ON STABILITY OF ERROR BOUNDS IN STATISTICAL LEARNING THEORY (Q3096969) (← links)
- Theory of Classification: a Survey of Some Recent Advances (Q3373749) (← links)
- (Q4558508) (← links)
- Estimating Individualized Treatment Rules Using Outcome Weighted Learning (Q4648555) (← links)
- Proximal Activation of Smooth Functions in Splitting Algorithms for Convex Image Recovery (Q5109277) (← links)
- (Q5148978) (← links)
- Coefficient-based regularization network with variance loss for error (Q5150110) (← links)
- Posterior consistency of semi-supervised regression on graphs (Q5157865) (← links)
- Bayesian Additive Machine: classification with a semiparametric discriminant function (Q5222366) (← links)
- Robust Support Vector Machines for Classification with Nonconvex and Smooth Losses (Q5380444) (← links)
- Optimal estimation for large-eddy simulation of turbulence and application to the analysis of subgrid models (Q5756127) (← links)
- Comment (Q5891754) (← links)
- Comment (Q5965645) (← links)
- Error analysis of classification learning algorithms based on LUMs loss (Q6112861) (← links)
- Learning performance of uncentered kernel-based principal component analysis (Q6168951) (← links)
- Structure learning via unstructured kernel-based M-estimation (Q6184881) (← links)
- Linear twin quadratic surface support vector regression (Q6534532) (← links)
- Nonparametric augmented probability weighting with sparsity (Q6554241) (← links)
- Support vector machine in big data: smoothing strategy and adaptive distributed inference (Q6643223) (← links)
- Sparse additive support vector machines in bounded variation space (Q6663350) (← links)
