The following pages link to (Q3093282):
Displaying 50 items.
- Multi-penalty regularization in learning theory (Q306697) (← links)
- Multi-output learning via spectral filtering (Q439000) (← links)
- Kernel methods in system identification, machine learning and function estimation: a survey (Q462325) (← links)
- Optimal rates for regularization of statistical inverse learning problems (Q667648) (← links)
- Consistent learning by composite proximal thresholding (Q681492) (← links)
- Regression learning based on incomplete relationships between attributes (Q781025) (← links)
- Geometry on probability spaces (Q843724) (← links)
- On regularization algorithms in learning theory (Q870339) (← links)
- On spectral windows in supervised learning from data (Q1675817) (← links)
- Generalized Kalman smoothing: modeling and algorithms (Q1678609) (← links)
- Mini-workshop: Deep learning and inverse problems. Abstracts from the mini-workshop held March 4--10, 2018 (Q1731979) (← links)
- Optimal filters from calibration data for image deconvolution with data acquisition error (Q1932994) (← links)
- Adaptive kernel methods using the balancing principle (Q1959089) (← links)
- On a regularization of unsupervised domain adaptation in RKHS (Q2075006) (← links)
- An elementary analysis of ridge regression with random design (Q2080945) (← links)
- Estimating adsorption isotherm parameters in chromatography via a virtual injection promoting double feed-forward neural network (Q2082130) (← links)
- Machine learning with kernels for portfolio valuation and risk management (Q2120539) (← links)
- Feasibility-based fixed point networks (Q2138454) (← links)
- Learning from non-random data in Hilbert spaces: an optimal recovery perspective (Q2143167) (← links)
- Manifold regularization based on Nyström type subsampling (Q2175018) (← links)
- Smoothed residual stopping for statistical inverse problems via truncated SVD estimation (Q2209816) (← links)
- Kernel variable selection for multicategory support vector machines (Q2237819) (← links)
- Efficient regularized least-squares algorithms for conditional ranking on relational data (Q2251443) (← links)
- Diffusion maps for changing data (Q2252180) (← links)
- Random discretization of the finite Fourier transform and related kernel random matrices (Q2310829) (← links)
- A consistent and numerically efficient variable selection method for sparse Poisson regression with applications to learning and signal recovery (Q2329779) (← links)
- Complexity control in statistical learning (Q2371223) (← links)
- Multi-task learning via linear functional strategy (Q2407408) (← links)
- Statistical performance of support vector machines (Q2426613) (← links)
- Error analysis on regularized regression based on the maximum correntropy criterion (Q2668572) (← links)
- Convergence rates of kernel conjugate gradient for random design regression (Q2835985) (← links)
- Kernel regression, minimax rates and effective dimensionality: Beyond the regular case (Q3298576) (← links)
- Convergence Rates of Spectral Regularization Methods: A Comparison between Ill-Posed Inverse Problems and Statistical Kernel Learning (Q3386994) (← links)
- Machine learning from examples: Inductive and Lazy methods (Q3840867) (← links)
- Kernel partial least squares for stationary data (Q4637047) (← links)
- (Q4637072) (← links)
- (Q4871675) (← links)
- (Q4998979) (← links)
- Distributed least squares prediction for functional linear regression* (Q5019925) (← links)
- Regularization: From Inverse Problems to Large-Scale Machine Learning (Q5028166) (← links)
- Two-Layer Neural Networks with Values in a Banach Space (Q5055293) (← links)
- Wasserstein-Based Projections with Applications to Inverse Problems (Q5074785) (← links)
- Representation and reconstruction of covariance operators in linear inverse problems (Q5117389) (← links)
- (Q5148996) (← links)
- Ensemble Kalman inversion: a derivative-free technique for machine learning tasks (Q5197869) (← links)
- Thresholded spectral algorithms for sparse approximations (Q5267950) (← links)
- Nyström type subsampling analyzed as a regularized projection (Q5348003) (← links)
- Learning regularization parameters for general-form Tikhonov (Q5348006) (← links)
- Learning theory of distributed spectral algorithms (Q5348011) (← links)
- A Study on Regularization for Discrete Inverse Problems with Model-Dependent Noise (Q5359494) (← links)
