The following pages link to (Q3174075):
Displaying 37 items.
- Error bounds for \(l^p\)-norm multiple kernel learning with least square loss (Q448851) (← links)
- Classification with Gaussians and convex loss. II: Improving error bounds by noise conditions (Q547325) (← links)
- Learning the coordinate gradients (Q695644) (← links)
- Learnability with respect to fixed distributions (Q809614) (← links)
- Multi-kernel regularized classifiers (Q870343) (← links)
- Orthogonality from disjoint support in reproducing kernel Hilbert spaces (Q952158) (← links)
- Parzen windows for multi-class classification (Q958247) (← links)
- Learning and approximation by Gaussians on Riemannian manifolds (Q960002) (← links)
- Learning rates of multi-kernel regularized regression (Q974504) (← links)
- Nonlinear approximation using Gaussian kernels (Q982495) (← links)
- High order Parzen windows and randomized sampling (Q1047130) (← links)
- Fast learning rate of non-sparse multiple kernel learning and optimal regularization strategies (Q1657947) (← links)
- Optimal regression rates for SVMs using Gaussian kernels (Q1951100) (← links)
- Conditional quantiles with varying Gaussians (Q1955538) (← links)
- Unregularized online algorithms with varying Gaussians (Q2035494) (← links)
- Distributed regularized least squares with flexible Gaussian kernels (Q2036424) (← links)
- Convergence of online pairwise regression learning with quadratic loss (Q2191834) (← links)
- Quantitative convergence analysis of kernel based large-margin unified machines (Q2191836) (← links)
- Learning performance of regularized regression with multiscale kernels based on Markov observations (Q2244161) (← links)
- Approximation of kernel matrices by circulant matrices and its application in kernel selection methods (Q2266837) (← links)
- Least square regularized regression for multitask learning (Q2319008) (← links)
- Learning with sample dependent hypothesis spaces (Q2389476) (← links)
- The optimal solution of multi-kernel regularization learning (Q2392006) (← links)
- Convergence analysis of online algorithms (Q2454719) (← links)
- Error analysis on regularized regression based on the maximum correntropy criterion (Q2668572) (← links)
- Summation of Gaussian shifts as Jacobi's third theta function (Q2668574) (← links)
- On extension theorems and their connection to universal consistency in machine learning (Q2835986) (← links)
- Error bounds for learning the kernel (Q2835989) (← links)
- Error Estimates for Multivariate Regression on Discretized Function Spaces (Q4976112) (← links)
- (Q5148999) (← links)
- Refined Rademacher Chaos Complexity Bounds with Applications to the Multikernel Learning Problem (Q5378343) (← links)
- A Note on Support Vector Machines with Polynomial Kernels (Q5380381) (← links)
- Learning Rates for Classification with Gaussian Kernels (Q5380881) (← links)
- Online Classification with Varying Gaussians (Q5851123) (← links)
- Optimal learning with Gaussians and correntropy loss (Q5856264) (← links)
- Some properties of Gaussian reproducing kernel Hilbert spaces and their implications for function approximation and learning theory (Q5962345) (← links)
- Maximum correntropy criterion regression models with tending-to-zero scale parameters (Q6541933) (← links)
