The following pages link to Support Vector Machines (Q3499201):
Displaying 50 items.
- Kernel Methods for the Approximation of Nonlinear Systems (Q5348477) (← links)
- Multiple Spectral Kernel Learning and a Gaussian Complexity Computation (Q5378237) (← links)
- Density-Difference Estimation (Q5378275) (← links)
- Support vector machines regression with unbounded sampling (Q5379431) (← links)
- Refined Generalization Bounds of Gradient Learning over Reproducing Kernel Hilbert Spaces (Q5380250) (← links)
- A Note on Support Vector Machines with Polynomial Kernels (Q5380381) (← links)
- Filtering with State-Observation Examples via Kernel Monte Carlo Filter (Q5380397) (← links)
- Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery (Q5380402) (← links)
- Online Pairwise Learning Algorithms (Q5380417) (← links)
- Robust Support Vector Machines for Classification with Nonconvex and Smooth Losses (Q5380444) (← links)
- Learning Theory Estimates with Observations from General Stationary Stochastic Processes (Q5380606) (← links)
- Analysis of Online Composite Mirror Descent Algorithm (Q5380674) (← links)
- Learning Rates for Classification with Gaussian Kernels (Q5380881) (← links)
- A representer theorem for deep kernel learning (Q5381118) (← links)
- Generalized Mercer Kernels and Reproducing Kernel Banach Spaces (Q5383917) (← links)
- (Q5754357) (← links)
- Accelerated Iterative Regularization via Dual Diagonal Descent (Q5853571) (← links)
- Optimal learning with Gaussians and correntropy loss (Q5856264) (← links)
- Generalized representer theorems in Banach spaces (Q5856265) (← links)
- Learning rates for partially linear support vector machine in high dimensions (Q5856267) (← links)
- Off-Policy Estimation of Long-Term Average Outcomes With Applications to Mobile Health (Q5857153) (← links)
- Approximative Policy Iteration for Exit Time Feedback Control Problems Driven by Stochastic Differential Equations using Tensor Train Format (Q5865245) (← links)
- For interpolating kernel machines, minimizing the norm of the ERM solution maximizes stability (Q5873932) (← links)
- An approach to the Gaussian RBF kernels via Fock spaces (Q5884798) (← links)
- Tensors in computations (Q5887832) (← links)
- Convergence and finite sample approximations of entropic regularized Wasserstein distances in Gaussian and RKHS settings (Q5889893) (← links)
- Support vector machines learning noisy polynomial rules (Q5951425) (← links)
- On the convergence rate and some applications of regularized ranking algorithms (Q5963450) (← links)
- Computing functions of random variables via reproducing kernel Hilbert space representations (Q5963778) (← links)
- A spectral series approach to high-dimensional nonparametric regression (Q5965330) (← links)
- An Online Projection Estimator for Nonparametric Regression in Reproducing Kernel Hilbert Spaces (Q6039862) (← links)
- On the issue of optimum machine learning methods for filling and updating nuclear knowledge graphs (Q6040363) (← links)
- Hilbert–Schmidt regularity of symmetric integral operators on bounded domains with applications to SPDE approximations (Q6046012) (← links)
- Entropic regularization of Wasserstein distance between infinite-dimensional Gaussian measures and Gaussian processes (Q6046189) (← links)
- Generalization of the energy distance by Bernstein functions (Q6046199) (← links)
- Overcoming the timescale barrier in molecular dynamics: Transfer operators, variational principles and machine learning (Q6047503) (← links)
- Generalization of the HSIC and distance covariance using PDI kernels (Q6048904) (← links)
- Capacity dependent analysis for functional online learning algorithms (Q6051150) (← links)
- Data perturbations in stochastic generalized equations: statistical robustness in static and sample average approximated models (Q6052056) (← links)
- Spherical random sampling of localized functions on 𝕊ⁿ⁻¹ (Q6052158) (← links)
- Technical note—Knowledge gradient for selection with covariates: Consistency and computation (Q6053135) (← links)
- Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics (Q6053801) (← links)
- Receiver operating characteristic curves and confidence bands for support vector machines (Q6055500) (← links)
- Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation (Q6062242) (← links)
- A perspective on machine learning methods in turbulence modeling (Q6068270) (← links)
- Error guarantees for least squares approximation with noisy samples in domain adaptation (Q6071903) (← links)
- Fully corrective gradient boosting with squared hinge: fast learning rates and early stopping (Q6072435) (← links)
- Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator: concept for bias correction, assessment of surrogate reliability and its application to the carbon dioxide benchmark (Q6074252) (← links)
- Singular Dynamic Mode Decomposition (Q6076402) (← links)
- Fast convergence rates of deep neural networks for classification (Q6078714) (← links)
